personalized-medicine-eda.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Personalised Mecidine - Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Key Points for presentation\n",
    "- Describe the problem.\n",
    "- Describe the dataset using visualizations and statistical measures.\n",
    "- Describe the challenges you might face and propose some solutions.\n",
    "- Give a high level time plan for finishing the project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Goal of project\n",
    "Automatically classify genetic mutations that contribute to cancer tumor growth drivers) vs. presence of mutations that don’t affect the tumors (passengers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Gene', 'Variation', 'Class'], dtype='object')"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_train = pd.read_csv('genetic-data/training_variants')\n",
    "d_train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variable Understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ID - A specific gene of the specimen\n",
    "- Gene - The specific gene where the mutation took place\n",
    "- Variation - the nature of the mutation\n",
    "- Class - Class which we must predict\n",
    "    - between 1 and 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3321.000000</td>\n",
       "      <td>3321.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1660.000000</td>\n",
       "      <td>4.365854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>958.834449</td>\n",
       "      <td>2.309781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>830.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1660.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2490.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3320.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ID        Class\n",
       "count  3321.000000  3321.000000\n",
       "mean   1660.000000     4.365854\n",
       "std     958.834449     2.309781\n",
       "min       0.000000     1.000000\n",
       "25%     830.000000     2.000000\n",
       "50%    1660.000000     4.000000\n",
       "75%    2490.000000     7.000000\n",
       "max    3320.000000     9.000000"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get intitial statistics for columns\n",
    "d_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID            int64\n",
       "Gene         object\n",
       "Variation    object\n",
       "Class         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID           3321\n",
       "Gene          264\n",
       "Variation    2996\n",
       "Class           9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_train.apply(pd.Series.nunique)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now know that there are 3321 records in our training set - all of which are unique.\n",
    "Although we have 264 unique genes and 2996 unique variations.\n",
    "Lastly there are 9 classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3321 entries, 0 to 3320\n",
      "Data columns (total 4 columns):\n",
      "ID           3321 non-null int64\n",
      "Gene         3321 non-null object\n",
      "Variation    3321 non-null object\n",
      "Class        3321 non-null int64\n",
      "dtypes: int64(2), object(2)\n",
      "memory usage: 103.9+ KB\n"
     ]
    }
   ],
   "source": [
    "d_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Class Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# histogram of classes\n",
    "sns.distplot(d_train['Class']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Not normally distributed\n",
    "- Its 3 peaks seem to be class 1, 4, and 7?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: -0.113242\n",
      "Kurtosis: -1.352660\n"
     ]
    }
   ],
   "source": [
    "# skewness and kurtosis\n",
    "print(\"Skewness: %f\" % d_train['Class'].skew())\n",
    "print(\"Kurtosis: %f\" % d_train['Class'].kurt())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- slight negative skew tells us that it is slightly left tailed\n",
    "- negative value for kurtosis tells us it is slightly platykurtic (low)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding Gene/Variation as Categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gene</th>\n",
       "      <th>Variation</th>\n",
       "      <th>Class</th>\n",
       "      <th>GeneCode</th>\n",
       "      <th>VariationCode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>FAM58A</td>\n",
       "      <td>Truncating Mutations</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>2629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>CBL</td>\n",
       "      <td>W802*</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>2856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>CBL</td>\n",
       "      <td>Q249E</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>1897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>CBL</td>\n",
       "      <td>N454D</td>\n",
       "      <td>3</td>\n",
       "      <td>39</td>\n",
       "      <td>1667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>CBL</td>\n",
       "      <td>L399V</td>\n",
       "      <td>4</td>\n",
       "      <td>39</td>\n",
       "      <td>1447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID    Gene             Variation  Class  GeneCode  VariationCode\n",
       "0   0  FAM58A  Truncating Mutations      1        85           2629\n",
       "1   1     CBL                 W802*      2        39           2856\n",
       "2   2     CBL                 Q249E      2        39           1897\n",
       "3   3     CBL                 N454D      3        39           1667\n",
       "4   4     CBL                 L399V      4        39           1447"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# converting Gene and Variation to categories\n",
    "d_train['Gene'] = d_train['Gene'].astype('category')\n",
    "d_train['Variation'] = d_train['Variation'].astype('category')\n",
    "d_train['Gene'].head().cat.codes\n",
    "d_train['GeneCode'] = d_train['Gene'].cat.codes\n",
    "d_train['VariationCode'] = d_train['Variation'].cat.codes\n",
    "d_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AVWvaAzP/Zd79HyZZl+TjrbUvLk1JAACwuk17YObLt1QhAACwtXCxHgAA6Gza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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff43e9662b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.countplot(x=\"Class\", data=d_train)\n",
    "plt.ylabel('Frequency', fontsize=14)\n",
    "plt.xlabel('Class', fontsize=14)\n",
    "plt.title(\"Distribution of genetic mutation classes\", fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gene Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kSZrt/BMiSZIkqRKD9jlfzgR/QlRKKW20WhFJkiRJs9Sgfc7fBXwEOB/4XlP2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\n/X1TVueO8w4H4II93jZldXb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7Ht3E6O2knE9t481E031gnM9vp3fMXk7vF5GJvstyeser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\nkiRJPWE4lyRJknriL46Z4EMimwrfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff43e995550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.countplot(x=\"Gene\", data=d_train)\n",
    "plt.ylabel('Frequency', fontsize=14)\n",
    "plt.xlabel('Gene', fontsize=14)\n",
    "plt.title(\"Distribution of genetic mutation classes\", fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gene_group = d_train.groupby(\"Gene\")['Gene'].count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gene_group_asc = gene_group.sort_values(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bottom-10 Gene Occurances\n",
      " Gene\n",
      "KLF4      1\n",
      "FGF19     1\n",
      "FANCC     1\n",
      "FAM58A    1\n",
      "PAK1      1\n",
      "ERRFI1    1\n",
      "PAX8      1\n",
      "PIK3R3    1\n",
      "PMS1      1\n",
      "PPM1D     1\n",
      "Name: Gene, dtype: int64\n",
      "\n",
      "Top-10 Gene Occurances\n",
      " Gene\n",
      "BRCA1     264\n",
      "TP53      163\n",
      "EGFR      141\n",
      "PTEN      126\n",
      "BRCA2     125\n",
      "KIT        99\n",
      "BRAF       93\n",
      "ERBB2      69\n",
      "ALK        69\n",
      "PDGFRA     60\n",
      "Name: Gene, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Bottom-10 Gene Occurances\\n\", gene_group_asc[:10])\n",
    "print()\n",
    "print(\"Top-10 Gene Occurances\\n\", gene_group_asc.sort_values(ascending=False)[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top 20 Genes then Top 20 Variation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top-20 Gene Occurances\n",
      "\n",
      "<class 'pandas.core.series.Series'>\n",
      "[BRCA1, TP53, EGFR, PTEN, BRCA2, ..., MTOR, KRAS, MAP2K1, VHL, RET]\n",
      "Length: 20\n",
      "Categories (264, object): [ABL1, ACVR1, AGO2, AKT1, ..., WHSC1L1, XPO1, XRCC2, YAP1]\n",
      "<class 'pandas.core.categorical.Categorical'>\n"
     ]
    }
   ],
   "source": [
    "print(\"Top-20 Gene Occurances\\n\")\n",
    "top_20_genes = d_train.groupby(\"Gene\")['Gene'].count().sort_values(ascending=False)[:20]\n",
    "print(type(top_20_genes))\n",
    "top_20_genes = top_20_genes.index.values\n",
    "print(top_20_genes)\n",
    "print(type(top_20_genes))\n",
    "t20_genes_df = d_train[\n",
    "    (d_train['Gene'].isin(top_20_genes))\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff43e550748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate correlations\n",
    "corr = d_train.corr()\n",
    "\n",
    "# Heatmap\n",
    "sns.heatmap(corr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top-20 Variation Occurances within top-20 occuring genes\n",
      "\n",
      "Variation\n",
      "Amplification           10\n",
      "Deletion                 6\n",
      "Truncating Mutations     6\n",
      "Fusions                  4\n",
      "T167A                    2\n",
      "R841K                    2\n",
      "I31M                     2\n",
      "p61BRAF                  1\n",
      "K525E                    1\n",
      "K517R                    1\n",
      "K513R                    1\n",
      "K50L                     1\n",
      "K550_K558del             1\n",
      "K50E                     1\n",
      "K509I                    1\n",
      "K499E                    1\n",
      "K483M                    1\n",
      "K483E                    1\n",
      "K467T                    1\n",
      "K526E                    1\n",
      "Name: Variation, dtype: int64\n",
      "The list of top 20 vars subset:  [Amplification, Deletion, Truncating Mutations, Fusions, T167A, ..., K499E, K483M, K483E, K467T, K526E]\n",
      "Length: 20\n",
      "Categories (2996, object): [1_2009trunc, 2010_2471trunc, 256_286trunc, 3' Deletion, ..., YAP1-MAMLD1 Fusion, ZC3H7B-BCOR Fusion, ZNF198-FGFR1 Fusion, p61BRAF]\n"
     ]
    }
   ],
   "source": [
    "# of the top-20 genes - display the count of the variations\n",
    "print(\"Top-20 Variation Occurances within top-20 occuring genes\\n\")\n",
    "print(t20_genes_df.groupby(\"Variation\")['Variation'].count().sort_values(ascending=False)[:20])\n",
    "t20_vars_arr = t20_genes_df.groupby(\"Variation\")['Variation'].count().sort_values(ascending=False)[:20].index.values\n",
    "print(\"The list of top 20 vars subset: \", t20_vars_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ID    Gene             Variation  Class  GeneCode  VariationCode\n",
      "145    145    EGFR                 K467T      2        66           1263\n",
      "204    204    EGFR         Amplification      7        66            149\n",
      "260    260    EGFR                 R841K      1        66           2258\n",
      "417    417    TP53  Truncating Mutations      1       252           2629\n",
      "650    650  CDKN2A  Truncating Mutations      1        51           2629\n",
      "664    664  CDKN2A              Deletion      4        51            473\n",
      "744    744   ERBB2         Amplification      7        72            149\n",
      "880    880  PDGFRA                 R841K      7       179           2258\n",
      "912    912  PDGFRA         Amplification      7       179            149\n",
      "1015  1015    TSC2  Truncating Mutations      1       255           2629\n",
      "1036  1036    TSC2              Deletion      1       255            473\n",
      "1165  1165  PIK3CA                  I31M      5       181           1156\n",
      "1198  1198  PIK3CA         Amplification      2       181            149\n",
      "1452  1452   FGFR2                 K517R      1        94           1275\n",
      "1456  1456   FGFR2               Fusions      2        94            807\n",
      "1475  1475   FGFR2                 K526E      2        94           1277\n",
      "1479  1479   FGFR2                 K525E      7        94           1276\n",
      "1538  1538     ALK         Amplification      2         6            149\n",
      "1560  1560     ALK               Fusions      2         6            807\n",
      "1595  1595     VHL  Truncating Mutations      1       258           2629\n",
      "1627  1627     VHL              Deletion      1       258            473\n",
      "1646  1646    FLT3                 T167A      3        98           2513\n",
      "1686  1686    FLT3         Amplification      7        98            149\n",
      "2007  2007  MAP2K1         Amplification      6       140            149\n",
      "2185  2185    PTEN                 T167A      4       196           2513\n",
      "2191  2191    PTEN              Deletion      4       196            473\n",
      "2273  2273    PTEN  Truncating Mutations      1       196           2629\n",
      "2421  2421   BRCA1  Truncating Mutations      1        31           2629\n",
      "2428  2428   BRCA1                  K50L      6        31           1273\n",
      "2439  2439   BRCA1              Deletion      1        31            473\n",
      "2580  2580   BRCA1                  I31M      3        31           1156\n",
      "2658  2658   BRCA1                  K50E      6        31           1272\n",
      "2706  2706    BRAF                 K483E      2        30           1264\n",
      "2718  2718    BRAF               p61BRAF      7        30           2995\n",
      "2738  2738    BRAF                 K483M      2        30           1265\n",
      "2748  2748    BRAF         Amplification      7        30            149\n",
      "2753  2753    BRAF               Fusions      2        30            807\n",
      "2765  2765    BRAF                 K499E      7        30           1266\n",
      "2840  2840   BRCA2              Deletion      4        32            473\n",
      "2877  2877   BRCA2                 K513R      5        32           1274\n",
      "2970  2970     KIT          K550_K558del      7       130           1280\n",
      "2995  2995     KIT         Amplification      2       130            149\n",
      "2999  2999     KIT                 K509I      7       130           1271\n",
      "3151  3151    KRAS         Amplification      2       137            149\n",
      "3269  3269     RET               Fusions      2       213            807\n"
     ]
    }
   ],
   "source": [
    "t20_vars_df = t20_genes_df[\n",
    "    (t20_genes_df['Variation'].isin(t20_vars_arr))\n",
    "]\n",
    "print(t20_vars_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UnjZOyUUHF/owcBPZFSmQDbAx0K2dzMxGVDvfwqnopdOnkV369yJARDwO/FFZQZmZDVZ3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\nNjOrCCdkM7OK+P/qQXWLItg0ZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff43dfe9390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate correlations\n",
    "corr2 = t20_vars_df[['Class', 'GeneCode', 'VariationCode']].corr()\n",
    "\n",
    "# Heatmap\n",
    "sns.heatmap(corr2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variation Top 20 then Gene Top 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The list of top 20 vars subset:  ['Truncating Mutations' 'Deletion' 'Amplification' 'Fusions'\n",
      " 'Overexpression' 'G12V' 'E17K' 'T58I' 'Q61L' 'Q61R' 'Q61H'\n",
      " 'ETV6-NTRK3 Fusion' 'T286A' 'P34R' 'EWSR1-ETV1 Fusion' 'G12S' 'Y42C'\n",
      " 'Q61K' 'I31M' 'G12A']\n"
     ]
    }
   ],
   "source": [
    "t20_vars_arr = d_train.groupby(\"Variation\")['Variation'].count().sort_values(ascending=False)[:20].index.values\n",
    "print(\"The list of top 20 vars subset: \", t20_vars_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ID    Gene             Variation  Class  GeneCode  VariationCode\n",
      "204    204    EGFR         Amplification      7        66            149\n",
      "417    417    TP53  Truncating Mutations      1       252           2629\n",
      "650    650  CDKN2A  Truncating Mutations      1        51           2629\n",
      "664    664  CDKN2A              Deletion      4        51            473\n",
      "744    744   ERBB2         Amplification      7        72            149\n",
      "912    912  PDGFRA         Amplification      7       179            149\n",
      "1015  1015    TSC2  Truncating Mutations      1       255           2629\n",
      "1036  1036    TSC2              Deletion      1       255            473\n",
      "1165  1165  PIK3CA                  I31M      5       181           1156\n",
      "1198  1198  PIK3CA         Amplification      2       181            149\n",
      "1456  1456   FGFR2               Fusions      2        94            807\n",
      "1538  1538     ALK         Amplification      2         6            149\n",
      "1560  1560     ALK               Fusions      2         6            807\n",
      "1595  1595     VHL  Truncating Mutations      1       258           2629\n",
      "1627  1627     VHL              Deletion      1       258            473\n",
      "1686  1686    FLT3         Amplification      7        98            149\n",
      "2007  2007  MAP2K1         Amplification      6       140            149\n",
      "2191  2191    PTEN              Deletion      4       196            473\n",
      "2273  2273    PTEN  Truncating Mutations      1       196           2629\n",
      "2421  2421   BRCA1  Truncating Mutations      1        31           2629\n",
      "2439  2439   BRCA1              Deletion      1        31            473\n",
      "2580  2580   BRCA1                  I31M      3        31           1156\n",
      "2748  2748    BRAF         Amplification      7        30            149\n",
      "2753  2753    BRAF               Fusions      2        30            807\n",
      "2819  2819   BRCA2                  Y42C      5        32           2934\n",
      "2840  2840   BRCA2              Deletion      4        32            473\n",
      "2995  2995     KIT         Amplification      2       130            149\n",
      "3118  3118    KRAS                  Q61H      7       137           1931\n",
      "3140  3140    KRAS                  Q61L      7       137           1933\n",
      "3141  3141    KRAS                  T58I      7       137           2575\n",
      "3142  3142    KRAS                  G12S      7       137            843\n",
      "3147  3147    KRAS                  G12A      7       137            838\n",
      "3148  3148    KRAS                  P34R      7       137           1799\n",
      "3150  3150    KRAS                  G12V      7       137            844\n",
      "3151  3151    KRAS         Amplification      2       137            149\n",
      "3153  3153    KRAS                  Q61R      7       137           1934\n",
      "3269  3269     RET               Fusions      2       213            807\n",
      "Gene\n",
      "KRAS      9\n",
      "BRCA1     3\n",
      "BRAF      2\n",
      "VHL       2\n",
      "CDKN2A    2\n",
      "BRCA2     2\n",
      "TSC2      2\n",
      "ALK       2\n",
      "PIK3CA    2\n",
      "PTEN      2\n",
      "FLT3      1\n",
      "PDGFRA    1\n",
      "MAP2K1    1\n",
      "FGFR2     1\n",
      "RET       1\n",
      "ERBB2     1\n",
      "KIT       1\n",
      "EGFR      1\n",
      "TP53      1\n",
      "FANCC     0\n",
      "Name: Gene, dtype: int64\n",
      "        ID    Gene             Variation  Class  GeneCode  VariationCode\n",
      "204    204    EGFR         Amplification      7        66            149\n",
      "417    417    TP53  Truncating Mutations      1       252           2629\n",
      "650    650  CDKN2A  Truncating Mutations      1        51           2629\n",
      "664    664  CDKN2A              Deletion      4        51            473\n",
      "744    744   ERBB2         Amplification      7        72            149\n",
      "912    912  PDGFRA         Amplification      7       179            149\n",
      "1015  1015    TSC2  Truncating Mutations      1       255           2629\n",
      "1036  1036    TSC2              Deletion      1       255            473\n",
      "1165  1165  PIK3CA                  I31M      5       181           1156\n",
      "1198  1198  PIK3CA         Amplification      2       181            149\n",
      "1456  1456   FGFR2               Fusions      2        94            807\n",
      "1538  1538     ALK         Amplification      2         6            149\n",
      "1560  1560     ALK               Fusions      2         6            807\n",
      "1595  1595     VHL  Truncating Mutations      1       258           2629\n",
      "1627  1627     VHL              Deletion      1       258            473\n",
      "1686  1686    FLT3         Amplification      7        98            149\n",
      "2007  2007  MAP2K1         Amplification      6       140            149\n",
      "2191  2191    PTEN              Deletion      4       196            473\n",
      "2273  2273    PTEN  Truncating Mutations      1       196           2629\n",
      "2421  2421   BRCA1  Truncating Mutations      1        31           2629\n",
      "2439  2439   BRCA1              Deletion      1        31            473\n",
      "2580  2580   BRCA1                  I31M      3        31           1156\n",
      "2748  2748    BRAF         Amplification      7        30            149\n",
      "2753  2753    BRAF               Fusions      2        30            807\n",
      "2819  2819   BRCA2                  Y42C      5        32           2934\n",
      "2840  2840   BRCA2              Deletion      4        32            473\n",
      "2995  2995     KIT         Amplification      2       130            149\n",
      "3118  3118    KRAS                  Q61H      7       137           1931\n",
      "3140  3140    KRAS                  Q61L      7       137           1933\n",
      "3141  3141    KRAS                  T58I      7       137           2575\n",
      "3142  3142    KRAS                  G12S      7       137            843\n",
      "3147  3147    KRAS                  G12A      7       137            838\n",
      "3148  3148    KRAS                  P34R      7       137           1799\n",
      "3150  3150    KRAS                  G12V      7       137            844\n",
      "3151  3151    KRAS         Amplification      2       137            149\n",
      "3153  3153    KRAS                  Q61R      7       137           1934\n",
      "3269  3269     RET               Fusions      2       213            807\n"
     ]
    }
   ],
   "source": [
    "t20_vars_df = t20_genes_df[\n",
    "    (t20_genes_df['Variation'].isin(t20_vars_arr))\n",
    "]\n",
    "print(t20_vars_df)\n",
    "t20_genes_arr = t20_vars_df.groupby(\"Gene\")['Gene'].count().sort_values(ascending=False)[:20]\n",
    "print(t20_genes_arr)\n",
    "top_20_genes = t20_genes_arr.index.values\n",
    "t20_genes_df = t20_vars_df[\n",
    "    (t20_vars_df['Gene'].isin(top_20_genes))\n",
    "]\n",
    "print(t20_genes_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3i2RTOg6aCOwJHAR8sl7lZmZFa2H3wiJgpqQdJE0Ejmb9fIik3clGdR0REU/mia/eON2J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\nNTMr0P8HINsYSxnA1gYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff43e5bdc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate correlations\n",
    "corr3 = t20_genes_df[['Class', 'GeneCode', 'VariationCode']].corr()\n",
    "\n",
    "# Heatmap\n",
    "sns.heatmap(corr3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top 20 Genes Class Distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fresh Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gene</th>\n",
       "      <th>Variation</th>\n",
       "      <th>Class</th>\n",
       "      <th>GeneCode</th>\n",
       "      <th>VariationCode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>FAM58A</td>\n",
       "      <td>Truncating Mutations</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>2629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>CBL</td>\n",
       "      <td>W802*</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>2856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>CBL</td>\n",
       "      <td>Q249E</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>1897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>CBL</td>\n",
       "      <td>N454D</td>\n",
       "      <td>3</td>\n",
       "      <td>39</td>\n",
       "      <td>1667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>CBL</td>\n",
       "      <td>L399V</td>\n",
       "      <td>4</td>\n",
       "      <td>39</td>\n",
       "      <td>1447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID    Gene             Variation  Class  GeneCode  VariationCode\n",
       "0   0  FAM58A  Truncating Mutations      1        85           2629\n",
       "1   1     CBL                 W802*      2        39           2856\n",
       "2   2     CBL                 Q249E      2        39           1897\n",
       "3   3     CBL                 N454D      3        39           1667\n",
       "4   4     CBL                 L399V      4        39           1447"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_train = pd.read_csv('genetic-data/training_variants')\n",
    "# converting Gene and Variation to categories\n",
    "d_train['Gene'] = d_train['Gene'].astype('category')\n",
    "d_train['Variation'] = d_train['Variation'].astype('category')\n",
    "d_train['Gene'].head().cat.codes\n",
    "d_train['GeneCode'] = d_train['Gene'].cat.codes\n",
    "d_train['VariationCode'] = d_train['Variation'].cat.codes\n",
    "d_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting top 20 genes from fresh dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene\n",
      "BRCA1     264\n",
      "TP53      163\n",
      "EGFR      141\n",
      "PTEN      126\n",
      "BRCA2     125\n",
      "KIT        99\n",
      "BRAF       93\n",
      "ERBB2      69\n",
      "ALK        69\n",
      "PDGFRA     60\n",
      "PIK3CA     56\n",
      "CDKN2A     52\n",
      "FGFR2      50\n",
      "FLT3       49\n",
      "TSC2       47\n",
      "MTOR       45\n",
      "KRAS       44\n",
      "MAP2K1     43\n",
      "VHL        41\n",
      "RET        40\n",
      "Name: Gene, dtype: int64\n",
      "        ID  Gene          Variation  Class  GeneCode  VariationCode\n",
      "138    138  EGFR   L747_T751delinsP      7        66           1519\n",
      "139    139  EGFR       S752_I759del      2        66           2465\n",
      "140    140  EGFR              I491M      5        66           1168\n",
      "141    141  EGFR       D770_P772dup      7        66            421\n",
      "142    142  EGFR              G719A      7        66            975\n",
      "143    143  EGFR              G735S      7        66            980\n",
      "144    144  EGFR       N771_H773dup      7        66           1707\n",
      "145    145  EGFR              K467T      2        66           1263\n",
      "146    146  EGFR     E746_T751insIP      7        66            641\n",
      "147    147  EGFR      D770_N771insD      7        66            418\n",
      "148    148  EGFR              G724S      7        66            979\n",
      "149    149  EGFR       K745_A750del      7        66           1322\n",
      "150    150  EGFR           EGFRvIII      7        66            674\n",
      "151    151  EGFR              V765A      2        66           2786\n",
      "152    152  EGFR            EGFRvII      7        66            673\n",
      "153    153  EGFR              L858M      2        66           1536\n",
      "154    154  EGFR              H870R      5        66           1098\n",
      "155    155  EGFR              E758G      2        66            642\n",
      "156    156  EGFR              H773L      7        66           1088\n",
      "157    157  EGFR              T710A      2        66           2587\n",
      "158    158  EGFR              L747P      7        66           1510\n",
      "159    159  EGFR              E709V      2        66            624\n",
      "160    160  EGFR              D761Y      2        66            414\n",
      "161    161  EGFR              K806A      2        66           1330\n",
      "162    162  EGFR              H773Y      2        66           1089\n",
      "163    163  EGFR              V851A      4        66           2804\n",
      "164    164  EGFR  EGFR-RAD51 Fusion      7        66            672\n",
      "165    165  EGFR    D770_N771insNPG      7        66            419\n",
      "166    166  EGFR       E746_A750del      7        66            635\n",
      "167    167  EGFR              A864T      7        66            134\n",
      "...    ...   ...                ...    ...       ...            ...\n",
      "3267  3267   RET              D631G      5       213            401\n",
      "3268  3268   RET              E921K      4       213            666\n",
      "3269  3269   RET            Fusions      2       213            807\n",
      "3270  3270   RET              S904F      2       213           2481\n",
      "3271  3271   RET              L790F      7       213           1527\n",
      "3272  3272   RET              R873Q      4       213           2262\n",
      "3273  3273   RET              Y806C      2       213           2978\n",
      "3274  3274   RET              S765P      4       213           2467\n",
      "3275  3275   RET              V804L      2       213           2798\n",
      "3276  3276   RET              R897Q      4       213           2270\n",
      "3277  3277   RET              D631A      5       213            400\n",
      "3278  3278   RET              A883F      7       213            135\n",
      "3279  3279   RET              C634S      7       213            244\n",
      "3280  3280   RET              A919V      2       213            139\n",
      "3281  3281   RET              C634W      2       213            245\n",
      "3282  3282   RET              M980T      5       213           1617\n",
      "3283  3283   RET              E768D      2       213            644\n",
      "3284  3284   RET   KIF5B-RET Fusion      7       213           1345\n",
      "3285  3285   RET              R833C      2       213           2254\n",
      "3286  3286   RET              V804M      7       213           2799\n",
      "3287  3287   RET              M918V      2       213           1616\n",
      "3288  3288   RET              C630R      7       213            241\n",
      "3289  3289   RET              V648I      5       213           2770\n",
      "3290  3290   RET              I852M      2       213           1185\n",
      "3291  3291   RET              C620R      7       213            238\n",
      "3292  3292   RET              C634Y      7       213            246\n",
      "3293  3293   RET              V804G      1       213           2797\n",
      "3294  3294   RET              R886W      7       213           2268\n",
      "3295  3295   RET              F893L      4       213            789\n",
      "3296  3296   RET              Y791F      7       213           2975\n",
      "\n",
      "[1676 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "t20_genes_arr = d_train.groupby(\"Gene\")['Gene'].count().sort_values(ascending=False)[:20]\n",
    "print(t20_genes_arr)\n",
    "top_20_genes = t20_genes_arr.index.values\n",
    "t20_genes_df = d_train[\n",
    "    (d_train['Gene'].isin(top_20_genes))\n",
    "]\n",
    "print(t20_genes_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-67-3c40dd95a93f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m#        label.set_rotation(0)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m#plt.tight_layout()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfactorplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Gene\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Gene\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Class\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mt20_genes_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"count\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.6/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mfactorplot\u001b[0;34m(x, y, hue, data, row, col, col_wrap, estimator, ci, n_boot, units, order, hue_order, row_order, col_order, kind, size, aspect, orient, color, palette, legend, legend_out, sharex, sharey, margin_titles, facet_kws, **kwargs)\u001b[0m\n\u001b[1;32m   3509\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3510\u001b[0m     \u001b[0;31m# Draw the plot onto the facets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3511\u001b[0;31m     \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_dataframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplot_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mplot_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3512\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3513\u001b[0m     \u001b[0;31m# Special case axis labels for a count type plot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.6/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mmap_dataframe\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    795\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    796\u001b[0m             \u001b[0;31m# Draw the plot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 797\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_facet_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    798\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    799\u001b[0m         \u001b[0;31m# Finalize the annotations and layout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.6/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36m_facet_plot\u001b[0;34m(self, func, ax, plot_args, plot_kwargs)\u001b[0m\n\u001b[1;32m    813\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    814\u001b[0m         \u001b[0;31m# Draw the plot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 815\u001b[0;31m         \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mplot_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mplot_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    816\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    817\u001b[0m         \u001b[0;31m# Sort out the supporting information\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.6/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mcountplot\u001b[0;34m(x, y, hue, data, order, hue_order, orient, color, palette, saturation, dodge, ax, **kwargs)\u001b[0m\n\u001b[1;32m   3345\u001b[0m         \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3346\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3347\u001b[0;31m     \u001b[0mplotter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3348\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3349\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.6/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, ax, bar_kws)\u001b[0m\n\u001b[1;32m   1638\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbar_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1639\u001b[0m         \u001b[0;34m\"\"\"Make the plot.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1640\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_bars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbar_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1641\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mannotate_axes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1642\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morient\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"h\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.6/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mdraw_bars\u001b[0;34m(self, ax, kws)\u001b[0m\n\u001b[1;32m   1623\u001b[0m                 barfunc(offpos, self.statistic[:, j], self.nested_width,\n\u001b[1;32m   1624\u001b[0m                         \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malign\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"center\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1625\u001b[0;31m                         label=hue_level, **kws)\n\u001b[0m\u001b[1;32m   1626\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1627\u001b[0m                 \u001b[0;31m# Draw the confidence intervals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1708\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m   1709\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1710\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1711\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1712\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mbar\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2196\u001b[0m             \u001b[0mymin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mymin\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m0.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1e-100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2197\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataLim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintervaly\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mymin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mymax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2198\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautoscale_view\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2199\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2200\u001b[0m         \u001b[0mbar_container\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBarContainer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpatches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrorbar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mautoscale_view\u001b[0;34m(self, tight, scalex, scaley)\u001b[0m\n\u001b[1;32m   2240\u001b[0m             \u001b[0mstickies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msticky_edges\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0martist\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_children\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2241\u001b[0m             \u001b[0mx_stickies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msticky\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msticky\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstickies\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2242\u001b[0;31m             \u001b[0my_stickies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msticky\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msticky\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstickies\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2243\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_xscale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'log'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2244\u001b[0m                 \u001b[0mx_stickies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mxs\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mxs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx_stickies\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mxs\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#g = sns.FacetGrid(t20_genes_df, col=\"Class\", col_wrap=2, size=4, aspect=2, hue=\"Class\", margin_titles=True, sharex=False)\n",
    "#g.map(plt.hist, \"Gene\", alpha=0.7, edgecolor='#000000', align='left');\n",
    "#for ax in g.axes.flat:\n",
    "#    for label in ax.get_xticklabels():\n",
    "#        label.set_rotation(0)\n",
    "#plt.tight_layout()\n",
    "g = sns.factorplot(x=\"Gene\", hue=\"Gene\", col=\"Class\", data=t20_genes_df, kind=\"count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t20_genes_df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Natural Language Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd       \n",
    "#train = pd.read_csv(\"genetic-data/training_text\", header=0, sep='\\|\\|', engine='python')\n",
    "train = pd.read_csv(\"genetic-data/training_text\", sep='\\|\\|', skiprows=1, engine='python', names=['ID', 'Text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   ID                                               Text\n",
      "0   0  Cyclin-dependent kinases (CDKs) regulate a var...\n",
      "1   1   Abstract Background  Non-small cell lung canc...\n",
      "2   2   Abstract Background  Non-small cell lung canc...\n",
      "3   3  Recent evidence has demonstrated that acquired...\n",
      "4   4  Oncogenic mutations in the monomeric Casitas B...\n",
      "train shape =  (3321, 2)\n",
      "train index values =  ['ID' 'Text']\n"
     ]
    }
   ],
   "source": [
    "print(train.head())\n",
    "print(\"train shape = \", train.shape)\n",
    "print(\"train index values = \", train.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n"
     ]
    }
   ],
   "source": [
    "# Import BeautifulSoup into your workspace\n",
    "from bs4 import BeautifulSoup             \n",
    "\n",
    "# regex\n",
    "import re\n",
    "\n",
    "# nltk\n",
    "import nltk\n",
    "from nltk.corpus import stopwords # Import the stop word list\n",
    "print(stopwords.words(\"english\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def review_to_words( raw_review ):\n",
    "    # Function to convert a raw review to a string of words\n",
    "    # The input is a single string (a raw movie review), and \n",
    "    # the output is a single string (a preprocessed movie review)\n",
    "    #\n",
    "    # 1. Remove HTML\n",
    "    review_text = BeautifulSoup(raw_review, 'html.parser').get_text() \n",
    "    #\n",
    "    # 2. Remove non-letters        \n",
    "    letters_only = re.sub(\"[^a-zA-Z\\-]\", \" \", review_text) \n",
    "    #\n",
    "    # 3. Convert to lower case, split into individual words\n",
    "    words = letters_only.lower().split()                             \n",
    "    #\n",
    "    # 4. In Python, searching a set is much faster than searching\n",
    "    #   a list, so convert the stop words to a set\n",
    "    stops = set(stopwords.words(\"english\"))                  \n",
    "    # \n",
    "    # 5. Remove stop words\n",
    "    meaningful_words = [w for w in words if not w in stops]   \n",
    "    #\n",
    "    # 6. Join the words back into one string separated by space, \n",
    "    # and return the result.\n",
    "    return( \" \".join( meaningful_words ))   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3321\n",
      "Cleaning and parsing the training set movie reviews...\n",
      "\n",
      "Review 1000 of 3321\n",
      "\n",
      "Review 2000 of 3321\n",
      "\n",
      "Review 3000 of 3321\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#print(train[\"Text\"][1])\n",
    "# Get the number of reviews based on the dataframe column size\n",
    "num_reviews = train[\"Text\"].size\n",
    "print(num_reviews)\n",
    "\n",
    "print(\"Cleaning and parsing the training set movie reviews...\\n\")\n",
    "clean_train_reviews = []\n",
    "for i in range( 0,num_reviews ):\n",
    "    # If the index is evenly divisible by 1000, print a message\n",
    "    if( (i+1)%1000 == 0 ):\n",
    "        print(\"Review %d of %d\\n\" % ( i+1, num_reviews ))                                                                    \n",
    "    clean_train_reviews.append( review_to_words( train[\"Text\"][i] ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating the bag of words...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Creating the bag of words...\\n\")\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "# Initialize the \"CountVectorizer\" object, which is scikit-learn's\n",
    "# bag of words tool.  \n",
    "vectorizer = CountVectorizer(analyzer = \"word\",   \\\n",
    "                             tokenizer = None,    \\\n",
    "                             preprocessor = None, \\\n",
    "                             stop_words = None,   \\\n",
    "                             max_features = 7500) \n",
    "\n",
    "# fit_transform() does two functions: First, it fits the model\n",
    "# and learns the vocabulary; second, it transforms our training data\n",
    "# into feature vectors. The input to fit_transform should be a list of \n",
    "# strings.\n",
    "train_data_features = vectorizer.fit_transform(clean_train_reviews)\n",
    "\n",
    "# Numpy arrays are easy to work with, so convert the result to an \n",
    "# array\n",
    "train_data_features = train_data_features.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3321, 7500)\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(train_data_features.shape)\n",
    "print(type(train_data_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['aa', 'aaa', 'aac', 'aacr', 'aacrjournals', 'aag', 'ab', 'abbott', 'abbreviations', 'abc', 'abcam', 'abcb', 'abd', 'abdomen', 'abdominal', 'aberrant', 'aberrantly', 'aberration', 'aberrations', 'abh', 'abi', 'abilities', 'ability', 'abiraterone', 'abl', 'ablation', 'able', 'abnormal', 'abnormalities', 'abnormality', 'abolish', 'abolished', 'abolishes', 'abolishing', 'above', 'abraxas', 'abrogate', 'abrogated', 'abrogates', 'abrogation', 'absence', 'absent', 'absolute', 'absolutely', 'absorbance', 'abstract', 'abt', 'abundance', 'abundant', 'ac', 'aca', 'acc', 'accelerate', 'accelerated', 'accepted', 'acceptor', 'access', 'accessibility', 'accessible', 'accession', 'accommodate', 'accompanied', 'accompanying', 'accomplished', 'accordance', 'according', 'accordingly', 'account', 'accounted', 'accounting', 'accounts', 'accumulate', 'accumulated', 'accumulates', 'accumulating', 'accumulation', 'accuracy', 'accurate', 'accurately', 'acetate', 'acetic', 'acetyl', 'acetylated', 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'reached', 'reaching', 'reacp', 'reaction', 'reactions', 'reactivation', 'reactive', 'reactivity', 'read', 'reader', 'readily', 'reading', 'readout', 'reads', 'ready', 'reagent', 'reagents', 'real', 'rearranged', 'rearrangement', 'rearrangements', 'reason', 'reasonable', 'reasoned', 'reasons', 'recapitulate', 'recapitulated', 'receive', 'received', 'receiving', 'recent', 'recently', 'receptor', 'receptors', 'recessive', 'recipient', 'recipients', 'reciprocal', 'recist', 'reclassified', 'recognition', 'recognize', 'recognized', 'recognizes', 'recognizing', 'recombinant', 'recombinase', 'recombination', 'recombineering', 'recommendations', 'recommended', 'reconstituted', 'reconstitution', 'recorded', 'records', 'recovered', 'recovery', 'recruit', 'recruited', 'recruiting', 'recruitment', 'recruits', 'rectal', 'rectum', 'recurrence', 'recurrent', 'recurrently', 'recurring', 'red', 'redistribution', 'reduce', 'reduced', 'reduces', 'reducing', 'reduction', 'reductions', 'redundancy', 'redundant', 'ref', 'refer', 'reference', 'references', 'referred', 'refers', 'refined', 'refinement', 'reflect', 'reflected', 'reflecting', 'reflections', 'reflects', 'refractory', 'refs', 'refseq', 'reg', 'regard', 'regarded', 'regarding', 'regardless', 'regeneration', 'regimen', 'regimens', 'region', 'regional', 'regions', 'registry', 'regorafenib', 'regression', 'regressions', 'regular', 'regulate', 'regulated', 'regulates', 'regulating', 'regulation', 'regulator', 'regulators', 'regulatory', 'reintroduction', 'rela', 'relapse', 'relapsed', 'relate', 'related', 'relates', 'relation', 'relationship', 'relationships', 'relative', 'relatively', 'relatives', 'release', 'released', 'relevance', 'relevant', 'reliable', 'relies', 'relieve', 'rely', 'remain', 'remainder', 'remained', 'remaining', 'remains', 'remarkable', 'remarkably', 'reminiscent', 'remission', 'remodeling', 'removal', 'remove', 'removed', 'removes', 'removing', 'renal', 'render', 'rendered', 'rendering', 'renders', 'renewal', 'renilla', 'reorganization', 'repair', 'repaired', 'repeat', 'repeated', 'repeatedly', 'repeats', 'repetitive', 'replace', 'replaced', 'replacement', 'replacements', 'replacing', 'replicate', 'replicates', 'replication', 'replicative', 'report', 'reported', 'reportedly', 'reporter', 'reporters', 'reporting', 'reports', 'repository', 'represent', 'representation', 'representations', 'representative', 'represented', 'representing', 'represents', 'repress', 'repressed', 'represses', 'repression', 'repressive', 'repressor', 'reprints', 'reprobed', 'reproducibility', 'reproducible', 'request', 'requests', 'require', 'required', 'requirement', 'requirements', 'requires', 'requiring', 'res', 'rescue', 'rescued', 'rescues', 'research', 'researchers', 'resected', 'resection', 'resemble', 'resembles', 'resembling', 'resequencing', 'reserved', 'reside', 'resides', 'residual', 'residue', 'residues', 'resin', 'resistance', 'resistant', 'resolution', 'resolve', 'resolved', 'resonance', 'resource', 'resources', 'respect', 'respective', 'respectively', 'respiratory', 'respond', 'responded', 'responder', 'responders', 'responding', 'response', 'responses', 'responsible', 'responsive', 'responsiveness', 'rest', 'resting', 'restoration', 'restore', 'restored', 'restores', 'restoring', 'restricted', 'restriction', 'result', 'resultant', 'resulted', 'resulting', 'results', 'resuspended', 'ret', 'retain', 'retained', 'retaining', 'retains', 'retardation', 'retention', 'reticulocyte', 'retinal', 'retinoblastoma', 'retinoic', 'retrieval', 'retrieved', 'retrospective', 'retrospectively', 'retroviral', 'retrovirally', 'retrovirus', 'retroviruses', 'rev', 'reveal', 'revealed', 'revealing', 'reveals', 'reversal', 'reverse', 'reversed', 'reversible', 'reversion', 'review', 'reviewed', 'reviews', 'revised', 'rfs', 'rg', 'rhabdoid', 'rhabdomyosarcoma', 'rhd', 'rheb', 'rho', 'rhoa', 'ri', 'ribbon', 'ribose', 'ribosomal', 'rich', 'rictor', 'ridge', 'right', 'rights', 'rigid', 'rigidity', 'rigorous', 'rii', 'ring', 'rinsed', 'ripa', 'rise', 'risk', 'risks', 'rit', 'rizos', 'rmce', 'rms', 'rmsd', 'rna', 'rnai', 'rnapii', 'rnas', 'rnase', 'rneasy', 'rnf', 'ro', 'roberts', 'robust', 'robustly', 'roc', 'roche', 'rock', 'role', 'roles', 'room', 'root', 'ror', 'ros', 'rosa', 'rotation', 'roughly', 'round', 'rounds', 'routine', 'routinely', 'row', 'rows', 'rp', 'rpm', 'rpmi', 'rppa', 'rps', 'rr', 'rras', 'rs', 'rsk', 'rst', 'rt', 'rtk', 'rtks', 'rtnf', 'rule', 'ruled', 'rules', 'run', 'running', 'runs', 'runt', 'runx', 'ruxolitinib', 'rv', 'rwpe', 'sa', 'saccharomyces', 'sacrificed', 'safety', 'sali', 'saline', 'salivary', 'salt', 'sam', 'sample', 'samples', 'sampling', 'san', 'sandwich', 'sanger', 'santa', 'saos', 'sapiens', 'sara', 'sarcoma', 'sarcomas', 'sarcomatoid', 'sas', 'sasaki', 'saturation', 'sb', 'sbc', 'sbcl', 'sbcs', 'sbe', 'sc', 'scaffold', 'scaffolding', 'scale', 'scaled', 'scan', 'scanned', 'scanner', 'scanning', 'scans', 'scatter', 'scattered', 'scc', 'sccoht', 'sccs', 'scd', 'scei', 'scenario', 'scf', 'sch', 'schematic', 'scheme', 'schemes', 'schizosaccharomyces', 'scholar', 'school', 'sci', 'scid', 'science', 'sciences', 'scientific', 'scintillation', 'sclc', 'sclerosis', 'scnas', 'sco', 'scope', 'score', 'scored', 'scores', 'scoring', 'scp', 'scrambled', 'screen', 'screened', 'screening', 'screens', 'sd', 'sdc', 'sdm', 'sds', 'sdspage', 'se', 'search', 'searched', 'sec', 'second', 'secondary', 'seconds', 'secretase', 'secreted', 'secretion', 'secretory', 'section', 'sections', 'see', 'seeded', 'seeding', 'seem', 'seemed', 'seems', 'seen', 'segment', 'segments', 'segregated', 'segregates', 'segregation', 'seizures', 'select', 'selectable', 'selected', 'selecting', 'selection', 'selective', 'selectively', 'selectivity', 'self', 'selleck', 'selumetinib', 'sem', 'sema', 'semi', 'senescence', 'sense', 'sensitive', 'sensitivities', 'sensitivity', 'sensitize', 'sensitized', 'sensitizing', 'sensor', 'seoul', 'separate', 'separated', 'separately', 'separation', 'sepharose', 'september', 'seq', 'sequence', 'sequenced', 'sequencer', 'sequences', 'sequencing', 'sequenom', 'sequential', 'sequentially', 'sequestration', 'ser', 'serial', 'series', 'serine', 'serious', 'serous', 'serum', 'serve', 'served', 'server', 'serves', 'service', 'set', 'setd', 'sets', 'setting', 'settings', 'seven', 'seventy', 'several', 'severe', 'severely', 'severity', 'sex', 'sf', 'sg', 'sh', 'shaded', 'shape', 'shaped', 'share', 'shared', 'shares', 'sharp', 'shc', 'shed', 'sheet', 'sheets', 'shell', 'sherr', 'shh', 'shi', 'shibata', 'shift', 'shifted', 'shifting', 'shifts', 'shock', 'short', 'shortened', 'shorter', 'show', 'showed', 'showing', 'shown', 'shows', 'shp', 'shq', 'shrinkage', 'shrna', 'shrnas', 'si', 'siblings', 'side', 'sided', 'sideroblasts', 'sift', 'sigma', 'sign', 'signal', 'signaling', 'signalling', 'signals', 'signature', 'signatures', 'signifi', 'significance', 'significant', 'significantly', 'signs', 'silenced', 'silencing', 'silent', 'silico', 'silver', 'similar', 'similarities', 'similarity', 'similarly', 'simple', 'simplified', 'simply', 'simulated', 'simulation', 'simulations', 'simultaneous', 'simultaneously', 'since', 'singh', 'single', 'sip', 'sirna', 'sirnas', 'sister', 'site', 'sites', 'situ', 'situated', 'situation', 'six', 'sixteen', 'sixty', 'size', 'sized', 'sizes', 'sk', 'skbr', 'skeletal', 'ski', 'skin', 'skipping', 'skmel', 'skp', 'slc', 'slegtenhorst', 'slide', 'slides', 'slight', 'slightly', 'sloan', 'slow', 'slower', 'slowly', 'sm', 'smad', 'smads', 'small', 'smaller', 'smarca', 'smarcb', 'smc', 'smith', 'smo', 'smoked', 'smoker', 'smokers', 'smoking', 'smooth', 'smurf', 'snail', 'snap', 'snd', 'snf', 'snp', 'snps', 'snu', 'snv', 'snvs', 'so', 'society', 'socs', 'sodium', 'soft', 'software', 'sole', 'solely', 'solid', 'soluble', 'solution', 'solutions', 'solved', 'solvent', 'somatic', 'somatically', 'sometimes', 'somewhat', 'sonicated', 'sonication', 'soon', 'sorafenib', 'sorted', 'sorting', 'sos', 'sought', 'source', 'sources', 'sourceturn', 'south', 'southern', 'sox', 'sp', 'spa', 'space', 'span', 'spanish', 'spanning', 'spans', 'spatial', 'spatially', 'spearman', 'speci', 'special', 'species', 'specifi', 'specific', 'specifically', 'specificities', 'specificity', 'specified', 'specimen', 'specimens', 'spectra', 'spectral', 'spectrometry', 'spectroscopy', 'spectrum', 'speculate', 'speculated', 'spheres', 'spheroids', 'spin', 'spinal', 'spindle', 'spine', 'spite', 'spleen', 'splenic', 'splenomegaly', 'splice', 'spliced', 'spliceosome', 'splicing', 'split', 'spontaneous', 'spontaneously', 'spop', 'spopbtb', 'spopmath', 'spopwt', 'sporadic', 'spot', 'spots', 'spread', 'spreading', 'spreads', 'spry', 'spss', 'sq', 'sqcc', 'squamous', 'square', 'squared', 'squares', 'sr', 'src', 'srf', 'srsf', 'sry', 'ss', 'ssa', 'ssc', 'sscp', 'ssr', 'st', 'stability', 'stabilization', 'stabilize', 'stabilized', 'stabilizes', 'stabilizing', 'stable', 'stably', 'stacking', 'stag', 'stage', 'stages', 'staging', 'stain', 'stained', 'staining', 'standard', 'standardized', 'standards', 'star', 'stark', 'start', 'started', 'starting', 'starvation', 'starved', 'stat', 'stata', 'state', 'states', 'statistical', 'statistically', 'statistics', 'stature', 'status', 'ste', 'steady', 'stem', 'step', 'steps', 'stepwise', 'stereo', 'steric', 'sterically', 'sterile', 'steroid', 'sti', 'stick', 'still', 'stimulate', 'stimulated', 'stimulates', 'stimulating', 'stimulation', 'stimulatory', 'stimuli', 'stimulus', 'stk', 'stock', 'stoichiometry', 'stomach', 'stop', 'stopped', 'stored', 'strad', 'straightforward', 'strain', 'strains', 'strand', 'stranded', 'strands', 'stratagene', 'strategies', 'strategy', 'stratification', 'stratified', 'strength', 'streptavidin', 'streptomycin', 'stress', 'stresses', 'stretch', 'strict', 'strictly', 'striking', 'strikingly', 'stringent', 'stripped', 'strn', 'stroma', 'stromal', 'strong', 'stronger', 'strongest', 'strongly', 'structural', 'structurally', 'structure', 'structures', 'student', 'studied', 'studies', 'study', 'studying', 'su', 'sub', 'subcellular', 'subclonal', 'subclone', 'subcloned', 'subclones', 'subcloning', 'subcutaneous', 'subcutaneously', 'subdivided', 'subdomain', 'subdomains', 'subfamily', 'subg', 'subgroup', 'subgroups', 'subject', 'subjected', 'subjects', 'submission', 'submitted', 'submitters', 'suboptimal', 'subpopulation', 'subpopulations', 'subsequent', 'subsequently', 'subset', 'subsets', 'substantial', 'substantially', 'substitute', 'substituted', 'substituting', 'substitution', 'substitutions', 'substrate', 'substrates', 'subtle', 'subtype', 'subtypes', 'subunit', 'subunits', 'success', 'successful', 'successfully', 'successive', 'suffered', 'sufficient', 'sufficiently', 'sufu', 'suggest', 'suggested', 'suggesting', 'suggestion', 'suggestive', 'suggests', 'suitable', 'suite', 'sulfate', 'sulfoxide', 'sum', 'summaries', 'summarize', 'summarized', 'summarizes', 'summary', 'sumoylation', 'sun', 'sunitinib', 'sup', 'super', 'superbinder', 'superfamily', 'superimposed', 'superimposition', 'superior', 'supernatant', 'supernatants', 'superscript', 'supervised', 'supm', 'supp', 'suppl', 'supplement', 'supplemental', 'supplementary', 'supplemented', 'supplied', 'supply', 'support', 'supported', 'supporting', 'supportive', 'supports', 'suppress', 'suppressed', 'suppresses', 'suppressing', 'suppression', 'suppressive', 'suppressor', 'suppressors', 'surface', 'surfaces', 'surgery', 'surgical', 'surgically', 'surprising', 'surprisingly', 'surrogate', 'surrounded', 'surrounding', 'surveillance', 'survey', 'surveyor', 'survival', 'survive', 'survived', 'surviving', 'susceptibility', 'susceptible', 'suspected', 'suspended', 'suspension', 'sustain', 'sustained', 'suv', 'sv', 'sw', 'sweden', 'swi', 'swiss', 'switch', 'switched', 'switching', 'switzerland', 'sy', 'sybr', 'symbols', 'symmetry', 'symptomatic', 'symptoms', 'syndrome', 'syndromes', 'synergistic', 'synergistically', 'synergize', 'synergy', 'syngeneic', 'synonymous', 'synthase', 'synthesis', 'synthesized', 'synthetic', 'system', 'systematic', 'systematically', 'systemic', 'systems', 'ta', 'tab', 'tabdownload', 'table', 'tables', 'tacc', 'tad', 'tae', 'tag', 'tagged', 'tags', 'tail', 'tailed', 'taiwan', 'taiwanese', 'takara', 'take', 'taken', 'takes', 'taking', 'tal', 'talk', 'tamoxifen', 'tandem', 'tap', 'taq', 'taqman', 'target', 'targetable', 'targeted', 'targeting', 'targets', 'tartaglia', 'tat', 'tau', 'taxane', 'taz', 'tbr', 'tbrii', 'tbs', 'tbst', 'tc', 'tca', 'tcc', 'tcf', 'tcga', 'tcr', 'tcs', 'tct', 'td', 'te', 'technical', 'technically', 'technique', 'techniques', 'technologies', 'technology', 'tel', 'telangiectasia', 'teleconferences', 'telomerase', 'telomere', 'telomeres', 'telomeric', 'temperature', 'temperatures', 'template', 'templates', 'temporal', 'tempting', 'ten', 'tend', 'tended', 'tendency', 'tensin', 'tensins', 'tentatively', 'ter', 'term', 'termed', 'terminal', 'terminally', 'terminated', 'termination', 'terminator', 'termini', 'terminus', 'terms', 'ternary', 'tert', 'tertiary', 'test', 'tested', 'testing', 'testis', 'testosterone', 'tests', 'tet', 'tethered', 'tetracycline', 'tetramer', 'tetrameric', 'tetramerization', 'tetramers', 'tetraodon', 'texas', 'text', 'tf', 'tfap', 'tg', 'tgf', 'tgfb', 'tgfbr', 'tgg', 'tgt', 'th', 'thd', 'theoretical', 'theory', 'therapeutic', 'therapeutically', 'therapeutics', 'therapies', 'therapy', 'thereafter', 'thereby', 'therefore', 'thermal', 'thermo', 'thermodynamic', 'thick', 'thickness', 'thin', 'think', 'thioredoxin', 'third', 'thirds', 'thirteen', 'thirty', 'thomas', 'thompson', 'thoracic', 'thoroughly', 'though', 'thought', 'thousands', 'thr', 'three', 'threefold', 'threonine', 'threshold', 'thresholds', 'thrombocytopenia', 'through', 'throughout', 'throughput', 'thumbnail', 'thus', 'thymic', 'thymidine', 'thymine', 'thymocytes', 'thymus', 'thyroid', 'ti', 'tier', 'tiered', 'tight', 'tightly', 'time', 'times', 'timing', 'tins', 'tion', 'tip', 'tissue', 'tissues', 'titer', 'titration', 'tk', 'tkb', 'tkd', 'tki', 'tkis', 'tl', 'tls', 'tlx', 'tm', 'tma', 'tmprss', 'tn', 'tnbc', 'tnf', 'tnrc', 'tnt', 'to', 'tobacco', 'tof', 'together', 'tokyo', 'tolerance', 'tolerant', 'tolerated', 'tomography', 'took', 'tool', 'tools', 'top', 'topo', 'tor', 'torc', 'torin', 'total', 'totally', 'toward', 'towards', 'toxic', 'toxicities', 'toxicity', 'tp', 'tpip', 'tpm', 'tpr', 'tpte', 'tq', 'tr', 'traces', 'tract', 'traditional', 'traf', 'trafficking', 'trail', 'training', 'trametinib', 'trans', 'transactivate', 'transactivating', 'transactivation', 'transcribed', 'transcript', 'transcriptase', 'transcription', 'transcriptional', 'transcriptionally', 'transcriptome', 'transcripts', 'transduced', 'transducer', 'transducers', 'transduction', 'transfectant', 'transfectants', 'transfected', 'transfecting', 'transfection', 'transfections', 'transfer', 'transferase', 'transferred', 'transform', 'transformant', 'transformants', 'transformation', 'transformed', 'transforming', 'transforms', 'transgene', 'transgenic', 'transient', 'transiently', 'transition', 'transitions', 'translate', 'translated', 'translation', 'translational', 'translocate', 'translocated', 'translocates', 'translocation', 'translocations', 'transmembrane', 'transmission', 'transmitted', 'transphosphorylation', 'transplant', 'transplantation', 'transplanted', 'transport', 'transversion', 'transversions', 'trap', 'trastuzumab', 'trcn', 'trcp', 'tre', 'treat', 'treated', 'treating', 'treatment', 'treatments', 'tree', 'trend', 'trex', 'tri', 'trial', 'trials', 'triangle', 'triangles', 'tric', 'trigger', 'triggered', 'triggering', 'triggers', 'trimer', 'trimethylation', 'trinucleotide', 'triphosphate', 'triple', 'triplicate', 'triplicates', 'tris', 'trisomy', 'trisphosphate', 'triton', 'trizol', 'trk', 'trka', 'trkaiii', 'trkb', 'trkc', 'trough', 'trp', 'trr', 'true', 'truly', 'truncated', 'truncating', 'truncation', 'truncations', 'truseq', 'trx', 'trypan', 'trypsin', 'trypsinized', 'tryptic', 'tryptophan', 'ts', 'tsa', 'tsc', 'tt', 'ttc', 'ttf', 'ttg', 'ttp', 'ttt', 'tube', 'tuberin', 'tuberous', 'tubulin', 'tumor', 'tumoral', 'tumorigenesis', 'tumorigenic', 'tumorigenicity', 'tumors', 'tumour', 'tumourigenesis', 'tumours', 'tunel', 'turn', 'turned', 'turnover', 'tween', 'twelve', 'twenty', 'twice', 'two', 'twofold', 'tx', 'tyk', 'type', 'types', 'typical', 'typically', 'typing', 'tyr', 'tyrosine', 'tyrosines', 'ub', 'ubc', 'ubch', 'ubiquitin', 'ubiquitinated', 'ubiquitination', 'ubiquitous', 'ubiquitously', 'ubiquitylated', 'ubiquitylation', 'uc', 'ucsc', 'ucvs', 'ug', 'uk', 'ul', 'ultimately', 'ultracentrifugation', 'ultraviolet', 'um', 'umd', 'unable', 'unaffected', 'unambiguous', 'unambiguously', 'unavailable', 'unbiased', 'unbound', 'uncertain', 'uncertainty', 'unchanged', 'uncharacterized', 'unclassified', 'unclear', 'uncommon', 'uncontrolled', 'uncover', 'uncovered', 'undefined', 'under', 'undergo', 'undergoes', 'undergoing', 'undergone', 'underlie', 'underlies', 'underlined', 'underlying', 'underscore', 'understand', 'understanding', 'understood', 'undertaken', 'underwent', 'undetectable', 'undetected', 'undifferentiated', 'unexpected', 'unexpectedly', 'unfavorable', 'unfolded', 'unfolding', 'unfortunately', 'unidentified', 'uniform', 'uniformly', 'uninformative', 'unique', 'uniquely', 'unit', 'united', 'units', 'univariate', 'universal', 'university', 'unknown', 'unlabeled', 'unless', 'unlike', 'unlikely', 'unmethylated', 'unmodified', 'unmutated', 'unpaired', 'unphosphorylated', 'unpublished', 'unrelated', 'unreported', 'unresectable', 'unselected', 'unstable', 'unstimulated', 'unsupervised', 'untransfected', 'untranslated', 'untreated', 'unusual', 'up', 'updated', 'upn', 'upon', 'upper', 'upregulate', 'upregulated', 'upregulation', 'upstate', 'upstream', 'uptake', 'ura', 'uracil', 'urea', 'urinary', 'urothelial', 'us', 'usa', 'usage', 'use', 'used', 'useful', 'usefulness', 'uses', 'using', 'usually', 'ut', 'uterine', 'utility', 'utilized', 'utilizing', 'utr', 'utx', 'uv', 'uveal', 'uvs', 'ux', 'va', 'val', 'valencia', 'valid', 'validate', 'validated', 'validating', 'validation', 'validity', 'valine', 'valuable', 'value', 'values', 'van', 'variability', 'variable', 'variables', 'variably', 'variance', 'variant', 'variants', 'variation', 'variations', 'varied', 'varies', 'variety', 'various', 'vary', 'varying', 'vascular', 'vast', 'vav', 'vb', 'vbc', 'vcap', 'vcb', 'vec', 'vector', 'vectors', 'vegf', 'vegfa', 'vegfr', 'vehicle', 'vein', 'vemurafenib', 'ventricular', 'vera', 'verified', 'verify', 'versa', 'version', 'versions', 'versus', 'vertebrate', 'vertebrates', 'vertical', 'vessels', 'vgp', 'vhl', 'vi', 'via', 'viability', 'viable', 'vic', 'vice', 'vicinity', 'view', 'viewed', 'viewer', 'viewerdownload', 'views', 'viii', 'vimentin', 'vinorelbine', 'violet', 'viral', 'virtually', 'virtue', 'virus', 'viruses', 'visible', 'vismodegib', 'visual', 'visualization', 'visualize', 'visualized', 'visually', 'vital', 'vitro', 'vivo', 'vo', 'vol', 'volume', 'volumes', 'von', 'vorinostat', 'vp', 'vras', 'vs', 'vus', 'vuss', 'vx', 'vysis', 'waals', 'waf', 'walker', 'wall', 'waltham', 'wang', 'wanted', 'wapcre', 'warranted', 'warrants', 'wash', 'washed', 'washes', 'washing', 'washington', 'wat', 'water', 'wave', 'wavelength', 'way', 'ways', 'wb', 'wbc', 'wd', 'weak', 'weaken', 'weaker', 'weakly', 'web', 'weber', 'website', 'week', 'weekly', 'weeks', 'wei', 'weight', 'weighted', 'weinberg', 'well', 'wells', 'wes', 'west', 'western', 'westerveld', 'wgs', 'whereas', 'whereby', 'wherein', 'whether', 'white', 'whitney', 'whole', 'whose', 'wi', 'wide', 'widely', 'wider', 'widespread', 'width', 'wilcoxon', 'wild', 'wildtype', 'wiley', 'williams', 'wilms', 'window', 'windows', 'withdrawal', 'within', 'without', 'wk', 'wm', 'wnt', 'woman', 'women', 'words', 'work', 'working', 'world', 'worldwide', 'worse', 'worth', 'wortmannin', 'would', 'wound', 'wpd', 'written', 'wt', 'wtb', 'wtc', 'wu', 'ww', 'www', 'wz', 'xbai', 'xenograft', 'xenografts', 'xenopus', 'xfoxh', 'xhoi', 'xing', 'xl', 'xp', 'xpd', 'xpf', 'xpo', 'xr', 'xrcc', 'xu', 'xxl', 'xy', 'xylene', 'yang', 'yap', 'year', 'years', 'yeast', 'yellow', 'yes', 'yet', 'yfp', 'yield', 'yielded', 'yielding', 'yields', 'yl', 'ylp', 'york', 'young', 'younger', 'yph', 'yr', 'yu', 'yvma', 'yy', 'zbp', 'zd', 'zebrafish', 'zeiss', 'zero', 'zf', 'zhang', 'zhou', 'zinc', 'zipper', 'zn', 'znf', 'zo', 'zone', 'zr', 'zymed']\n"
     ]
    }
   ],
   "source": [
    "# Take a look at the words in the vocabulary\n",
    "vocab = vectorizer.get_feature_names()\n",
    "print(vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aa 3710\n",
      "aaa 290\n",
      "aac 313\n",
      "aacr 507\n",
      "aacrjournals 3233\n",
      "aag 492\n",
      "ab 1446\n",
      "abbott 201\n",
      "abbreviations 765\n",
      "abc 961\n",
      "abcam 579\n",
      "abcb 203\n",
      "abd 978\n",
      "abdomen 207\n",
      "abdominal 450\n",
      "aberrant 4301\n",
      "aberrantly 484\n",
      "aberration 567\n",
      "aberrations 2929\n",
      "abh 329\n",
      "abi 1369\n",
      "abilities 452\n",
      "ability 15652\n",
      "abiraterone 202\n",
      "abl 7634\n",
      "ablation 495\n",
      "able 5706\n",
      "abnormal 1879\n",
      "abnormalities 2907\n",
      "abnormality 623\n",
      "abolish 1245\n",
      "abolished 1296\n",
      "abolishes 407\n",
      "abolishing 201\n",
      "above 192\n",
      "abraxas 206\n",
      "abrogate 1057\n",
      "abrogated 1557\n",
      "abrogates 401\n",
      "abrogation 477\n",
      "absence 11187\n",
      "absent 1911\n",
      "absolute 710\n",
      "absolutely 257\n",
      "absorbance 347\n",
      "abstract 1114\n",
      "abt 224\n",
      "abundance 1215\n",
      "abundant 767\n",
      "ac 2110\n",
      "aca 290\n",
      "acc 1948\n",
      "accelerate 337\n",
      "accelerated 829\n",
      "accepted 907\n",
      "acceptor 717\n",
      "access 2918\n",
      "accessibility 248\n",
      "accessible 2579\n",
      "accession 1479\n",
      "accommodate 554\n",
      "accompanied 1291\n",
      "accompanying 305\n",
      "accomplished 218\n",
      "accordance 1563\n",
      "according 11154\n",
      "accordingly 1259\n",
      "account 2698\n",
      "accounted 694\n",
      "accounting 478\n",
      "accounts 830\n",
      "accumulate 647\n",
      "accumulated 627\n",
      "accumulates 302\n",
      "accumulating 360\n",
      "accumulation 3563\n",
      "accuracy 656\n",
      "accurate 635\n",
      "accurately 449\n",
      "acetate 799\n",
      "acetic 207\n",
      "acetyl 319\n",
      "acetylated 373\n",
      "acetylation 1315\n",
      "acetyltransferase 233\n",
      "acgh 452\n",
      "achievable 450\n",
      "achieve 1260\n",
      "achieved 3158\n",
      "achieving 245\n",
      "achondroplasia 200\n",
      "acid 20241\n",
      "acidic 1017\n",
      "acids 10541\n",
      "acinar 693\n",
      "acini 254\n",
      "acknowledgments 349\n",
      "acquire 534\n",
      "acquired 5537\n",
      "acquisition 1358\n",
      "acral 791\n",
      "across 4888\n",
      "act 3022\n",
      "actin 3105\n",
      "acting 998\n",
      "action 1804\n",
      "actionable 414\n",
      "actions 325\n",
      "activate 7551\n",
      "activated 15168\n",
      "activates 2568\n",
      "activating 15117\n",
      "activation 50252\n",
      "activator 1436\n",
      "activators 861\n",
      "active 16121\n",
      "actively 337\n",
      "activin 215\n",
      "activities 7370\n",
      "activity 76748\n",
      "acts 1394\n",
      "actual 374\n",
      "actually 801\n",
      "acute 3628\n",
      "acvr 876\n",
      "ad 1618\n",
      "adam 208\n",
      "adapted 315\n",
      "adapter 310\n",
      "adaptive 325\n",
      "adaptor 1203\n",
      "adaptors 322\n",
      "adc 554\n",
      "add 516\n",
      "added 4566\n",
      "addgene 295\n",
      "addicted 441\n",
      "addiction 446\n",
      "adding 798\n",
      "addition 18698\n",
      "additional 15391\n",
      "additionally 2452\n",
      "additive 336\n",
      "address 2213\n",
      "addressed 831\n",
      "adds 201\n",
      "ade 487\n",
      "adenine 431\n",
      "adeno 280\n",
      "adenocarcinoma 5922\n",
      "adenocarcinomas 2514\n",
      "adenoma 521\n",
      "adenomas 803\n",
      "adenomatous 344\n",
      "adenosine 452\n",
      "adenosquamous 219\n",
      "adenovirus 299\n",
      "adequate 511\n",
      "adherent 193\n",
      "adhesion 2169\n",
      "adipocytes 205\n",
      "adjacent 3169\n",
      "adjusted 853\n",
      "adjustments 189\n",
      "adjuvant 536\n",
      "administered 701\n",
      "administration 1219\n",
      "adopt 579\n",
      "adopted 221\n",
      "adopts 506\n",
      "adp 462\n",
      "adrenal 677\n",
      "adrenocortical 255\n",
      "adult 2306\n",
      "adults 780\n",
      "advance 199\n",
      "advanced 4094\n",
      "advances 422\n",
      "advantage 2801\n",
      "advantages 377\n",
      "adverse 1311\n",
      "advertisement 219\n",
      "ae 253\n",
      "aee 381\n",
      "aes 212\n",
      "af 4649\n",
      "afatinib 1292\n",
      "affect 10394\n",
      "affected 9043\n",
      "affecting 4012\n",
      "affects 1587\n",
      "affinities 558\n",
      "affinity 6173\n",
      "affymetrix 1011\n",
      "aforementioned 292\n",
      "african 513\n",
      "afs 202\n",
      "ag 1194\n",
      "aga 298\n",
      "agar 2857\n",
      "agarose 2362\n",
      "agc 314\n",
      "age 9176\n",
      "aged 481\n",
      "agency 313\n",
      "agent 1942\n",
      "agents 3718\n",
      "ages 355\n",
      "agg 259\n",
      "aggregate 512\n",
      "aggregated 265\n",
      "aggregates 791\n",
      "aggregating 272\n",
      "aggregation 2386\n",
      "aggressive 2176\n",
      "aggressiveness 220\n",
      "agilent 1168\n",
      "ago 1398\n",
      "agonist 715\n",
      "agonists 219\n",
      "agree 246\n",
      "agreed 190\n",
      "agreement 3562\n",
      "ags 257\n",
      "agvgd 437\n",
      "ahmadian 295\n",
      "aid 823\n",
      "aim 622\n",
      "aimed 703\n",
      "aims 203\n",
      "air 213\n",
      "akap 456\n",
      "akt 26630\n",
      "al 105056\n",
      "ala 5012\n",
      "alanine 1485\n",
      "albeit 975\n",
      "alberta 218\n",
      "albumin 543\n",
      "alcl 676\n",
      "aldrich 1399\n",
      "alectinib 782\n",
      "alexa 502\n",
      "algorithm 1664\n",
      "algorithms 1011\n",
      "align 1730\n",
      "aligned 1013\n",
      "alignment 3499\n",
      "alignments 1203\n",
      "aliquots 576\n",
      "alive 527\n",
      "alk 27162\n",
      "alka 476\n",
      "alkaline 337\n",
      "alkd 195\n",
      "alkf 857\n",
      "alkm 247\n",
      "alkr 267\n",
      "alkt 377\n",
      "alkwt 230\n",
      "all 3865\n",
      "allele 12773\n",
      "alleles 10675\n",
      "allelic 1892\n",
      "allogeneic 560\n",
      "allosteric 1987\n",
      "allow 2969\n",
      "allowed 2422\n",
      "allowing 1357\n",
      "allows 2204\n",
      "alls 234\n",
      "almost 2899\n",
      "alone 6434\n",
      "along 3243\n",
      "alpha 1937\n",
      "already 2406\n",
      "also 66229\n",
      "alt 447\n",
      "alter 3091\n",
      "alteration 3736\n",
      "alterations 15112\n",
      "altered 6195\n",
      "altering 926\n",
      "alternate 480\n",
      "alternative 6260\n",
      "alternatively 1643\n",
      "alters 677\n",
      "although 24452\n",
      "altogether 478\n",
      "alvaro 237\n",
      "alveolar 201\n",
      "always 1187\n",
      "ambiguous 200\n",
      "amenable 250\n",
      "america 281\n",
      "american 4188\n",
      "amersham 1437\n",
      "amide 660\n",
      "amino 28001\n",
      "aminoacid 308\n",
      "aml 10563\n",
      "amls 210\n",
      "among 13807\n",
      "amongst 250\n",
      "amount 3335\n",
      "amounts 2452\n",
      "amp 576\n",
      "ampk 321\n",
      "ampli 301\n",
      "amplicon 843\n",
      "amplicons 707\n",
      "amplification 14162\n",
      "amplifications 1930\n",
      "amplified 7168\n",
      "amplify 807\n",
      "amsterdam 518\n",
      "analog 237\n",
      "analogous 1247\n",
      "analogue 216\n",
      "analogy 230\n",
      "analyse 253\n",
      "analysed 2360\n",
      "analyses 10816\n",
      "analysis 59004\n",
      "analytical 330\n",
      "analyze 1680\n",
      "analyzed 17453\n",
      "analyzer 766\n",
      "analyzing 762\n",
      "anaplastic 1248\n",
      "ancestry 295\n",
      "anchorage 1873\n",
      "and 327\n",
      "anderson 306\n",
      "androgen 2636\n",
      "androgens 343\n",
      "anemia 841\n",
      "aneuploidy 292\n",
      "angiogenesis 1308\n",
      "angiogenic 517\n",
      "angle 220\n",
      "animal 1808\n",
      "animals 2707\n",
      "ankyrin 1067\n",
      "annealed 214\n",
      "annealing 716\n",
      "annexin 550\n",
      "annotated 643\n",
      "annotation 375\n",
      "annually 261\n",
      "anomalies 438\n",
      "another 8186\n",
      "anova 476\n",
      "antagonist 429\n",
      "antagonists 644\n",
      "antagonize 219\n",
      "anterior 210\n",
      "anti 22233\n",
      "antiandrogen 225\n",
      "antiangiogenic 207\n",
      "antiapoptotic 368\n",
      "antibiotic 255\n",
      "antibiotics 298\n",
      "antibodies 11367\n",
      "antibody 14488\n",
      "anticancer 584\n",
      "anticipate 404\n",
      "anticipated 407\n",
      "antigen 2060\n",
      "antigens 550\n",
      "antiphosphotyrosine 285\n",
      "antiproliferative 329\n",
      "antisense 1049\n",
      "antisera 267\n",
      "antiserum 398\n",
      "antitumor 1265\n",
      "ap 2672\n",
      "apart 920\n",
      "apc 3825\n",
      "apo 470\n",
      "apoptosis 10516\n",
      "apoptotic 3442\n",
      "apparatus 256\n",
      "apparent 2457\n",
      "apparently 1135\n",
      "appear 3031\n",
      "appearance 1123\n",
      "appeared 1992\n",
      "appears 4119\n",
      "appendix 1741\n",
      "applicable 562\n",
      "application 736\n",
      "applications 269\n",
      "applied 5473\n",
      "apply 518\n",
      "applying 431\n",
      "approach 5672\n",
      "approaches 3365\n",
      "appropriate 2524\n",
      "approval 886\n",
      "approved 3485\n",
      "approx 306\n",
      "approximate 216\n",
      "approximately 6577\n",
      "april 475\n",
      "aprotinin 360\n",
      "ar 11172\n",
      "araf 1664\n",
      "arbitrary 317\n",
      "architecture 617\n",
      "archival 282\n",
      "area 2531\n",
      "areas 1515\n",
      "arf 1584\n",
      "arfl 292\n",
      "arg 7196\n",
      "arginine 2112\n",
      "argue 347\n",
      "argues 340\n",
      "arguing 225\n",
      "arid 1601\n",
      "arise 1946\n",
      "arises 481\n",
      "arising 1396\n",
      "arm 1318\n",
      "arms 519\n",
      "aromatic 532\n",
      "arose 726\n",
      "around 2084\n",
      "arranged 260\n",
      "arrangement 490\n",
      "array 2676\n",
      "arrays 970\n",
      "arrest 3881\n",
      "arrested 242\n",
      "arrow 1038\n",
      "arrowhead 254\n",
      "arrowheads 332\n",
      "arrows 1379\n",
      "ars 598\n",
      "article 3063\n",
      "articles 462\n",
      "artifacts 366\n",
      "artifi 547\n",
      "artificial 500\n",
      "arv 548\n",
      "as 292\n",
      "ascertain 298\n",
      "ascertained 392\n",
      "ascopubs 302\n",
      "ascribed 202\n",
      "asd 2824\n",
      "ashkenazi 384\n",
      "asian 1034\n",
      "asians 233\n",
      "ask 215\n",
      "asked 539\n",
      "asn 2757\n",
      "asp 7108\n",
      "asparagine 277\n",
      "aspartate 376\n",
      "aspartic 768\n",
      "aspect 328\n",
      "aspects 1025\n",
      "assay 30980\n",
      "assayed 2437\n",
      "assays 26610\n",
      "assemble 270\n",
      "assembled 667\n",
      "assembly 2264\n",
      "assess 5973\n",
      "assessed 7636\n",
      "assessing 752\n",
      "assessment 5135\n",
      "assessments 325\n",
      "assign 491\n",
      "assigned 2022\n",
      "assignment 448\n",
      "assist 370\n",
      "assistance 1745\n",
      "assisted 233\n",
      "associate 1152\n",
      "associated 40420\n",
      "associates 756\n",
      "associating 212\n",
      "association 11027\n",
      "associations 818\n",
      "asstt 189\n",
      "assume 359\n",
      "assumed 1423\n",
      "assumes 248\n",
      "assuming 494\n",
      "assumption 551\n",
      "asterisk 513\n",
      "asterisks 801\n",
      "astrocytes 353\n",
      "astrocytoma 486\n",
      "astrocytomas 611\n",
      "asxl 1336\n",
      "asymmetric 573\n",
      "asymptomatic 217\n",
      "at 584\n",
      "ata 210\n",
      "ataxia 235\n",
      "atc 463\n",
      "atcc 804\n",
      "atf 400\n",
      "atg 928\n",
      "athymic 306\n",
      "atlas 843\n",
      "atm 2358\n",
      "atmosphere 261\n",
      "atom 991\n",
      "atomic 501\n",
      "atoms 1260\n",
      "atp 9361\n",
      "atpase 946\n",
      "atr 909\n",
      "atra 204\n",
      "atrx 1364\n",
      "attached 272\n",
      "attachment 249\n",
      "attack 224\n",
      "attempt 381\n",
      "attempted 502\n",
      "attempts 369\n",
      "attention 505\n",
      "attenuate 330\n",
      "attenuated 891\n",
      "attenuation 315\n",
      "attractive 677\n",
      "attributable 475\n",
      "attributed 883\n",
      "atypical 1106\n",
      "au 445\n",
      "augment 244\n",
      "augmented 362\n",
      "august 572\n",
      "aupd 244\n",
      "aurora 1419\n",
      "australia 261\n",
      "australian 196\n",
      "authenticated 254\n",
      "author 7695\n",
      "authors 1711\n",
      "autism 912\n",
      "auto 776\n",
      "autocrine 666\n",
      "autoimmune 218\n",
      "autoinhibited 595\n",
      "autoinhibition 587\n",
      "autoinhibitory 778\n",
      "automated 655\n",
      "autonomous 652\n",
      "autophagy 359\n",
      "autophosphorylated 303\n",
      "autophosphorylation 2891\n",
      "autoradiography 578\n",
      "autosomal 1052\n",
      "auxilin 200\n",
      "auxilins 198\n",
      "availability 869\n",
      "available 13805\n",
      "avenue 223\n",
      "average 4869\n",
      "averaged 266\n",
      "avian 210\n",
      "avoid 698\n",
      "avoiding 230\n",
      "away 967\n",
      "awd 547\n",
      "ax 577\n",
      "axin 546\n",
      "axis 1426\n",
      "axl 760\n",
      "ay 356\n",
      "az 244\n",
      "azd 1838\n",
      "ba 8296\n",
      "bac 2089\n",
      "bach 2102\n",
      "back 644\n",
      "backbone 1733\n",
      "background 2958\n",
      "backgrounds 471\n",
      "bacteria 554\n",
      "bacterial 1306\n",
      "bacterially 282\n",
      "baculovirus 393\n",
      "bad 402\n",
      "baf 4365\n",
      "bak 586\n",
      "balance 639\n",
      "balanced 308\n",
      "balb 301\n",
      "ball 232\n",
      "bam 442\n",
      "bamhi 827\n",
      "band 2442\n",
      "bands 2374\n",
      "bank 858\n",
      "bap 2879\n",
      "bar 1349\n",
      "barcode 472\n",
      "barcoded 252\n",
      "bard 3968\n",
      "barrier 261\n",
      "bars 3423\n",
      "basal 4601\n",
      "base 4040\n",
      "based 24113\n",
      "basel 346\n",
      "baseline 1622\n",
      "basement 239\n",
      "bases 908\n",
      "basic 1754\n",
      "basis 6675\n",
      "bat 326\n",
      "bax 1286\n",
      "bay 601\n",
      "bayes 352\n",
      "bb 407\n",
      "bc 1727\n",
      "bcc 992\n",
      "bccs 1418\n",
      "bcl 5776\n",
      "bcor 629\n",
      "bcr 5635\n",
      "bd 1588\n",
      "bead 322\n",
      "beads 2908\n",
      "bear 311\n",
      "bearing 2771\n",
      "became 728\n",
      "beckman 362\n",
      "become 1908\n",
      "becomes 793\n",
      "becoming 382\n",
      "becton 394\n",
      "began 324\n",
      "begin 247\n",
      "beginning 538\n",
      "begun 213\n",
      "behave 434\n",
      "behaved 329\n",
      "behaves 218\n",
      "behavior 997\n",
      "behaviour 277\n",
      "behind 342\n",
      "belgium 236\n",
      "believe 506\n",
      "believed 745\n",
      "belong 357\n",
      "belonging 319\n",
      "belongs 333\n",
      "beneficial 443\n",
      "benefit 3295\n",
      "benefits 257\n",
      "benign 2992\n",
      "besides 1141\n",
      "best 1997\n",
      "beta 3536\n",
      "bethesda 249\n",
      "better 3536\n",
      "bevacizumab 382\n",
      "beverly 396\n",
      "beyond 1268\n",
      "bgb 226\n",
      "bgj 272\n",
      "bh 855\n",
      "bi 414\n",
      "biallelic 905\n",
      "bias 870\n",
      "bic 1587\n",
      "bicalutamide 389\n",
      "bicc 222\n",
      "bid 651\n",
      "bidirectional 359\n",
      "big 236\n",
      "bigdye 342\n",
      "bilateral 719\n",
      "bim 1365\n",
      "bin 303\n",
      "binary 337\n",
      "bind 7341\n",
      "binding 62870\n",
      "binds 4664\n",
      "binomial 454\n",
      "bio 1357\n",
      "bioanalyzer 214\n",
      "biochemical 4930\n",
      "biochemically 241\n",
      "biochemicals 235\n",
      "biochemistry 364\n",
      "bioinformatic 693\n",
      "bioinformatics 630\n",
      "biolabs 464\n",
      "biologic 965\n",
      "biological 4727\n",
      "biologically 631\n",
      "biology 2010\n",
      "biomarker 637\n",
      "biomarkers 713\n",
      "biophysical 358\n",
      "biopsies 1753\n",
      "biopsy 2074\n",
      "biorad 294\n",
      "bioscience 245\n",
      "biosciences 1660\n",
      "biosystems 2308\n",
      "biotech 466\n",
      "biotechnology 2449\n",
      "biotin 438\n",
      "biotinylated 617\n",
      "birth 590\n",
      "bis 381\n",
      "bisphosphate 429\n",
      "bl 1947\n",
      "black 1744\n",
      "bladder 2629\n",
      "blast 840\n",
      "blasts 898\n",
      "bleeding 215\n",
      "blimp 1159\n",
      "blinded 227\n",
      "block 2575\n",
      "blockade 480\n",
      "blocked 1746\n",
      "blocking 1429\n",
      "blocks 1822\n",
      "blood 6345\n",
      "bloodjournal 311\n",
      "blosum 417\n",
      "blot 6841\n",
      "blots 2016\n",
      "blotted 623\n",
      "blotting 3915\n",
      "blue 4596\n",
      "bm 1339\n",
      "bmp 770\n",
      "bms 268\n",
      "bmt 247\n",
      "board 1200\n",
      "boards 234\n",
      "bodies 394\n",
      "body 1227\n",
      "boehringer 235\n",
      "boiled 414\n",
      "boiling 203\n",
      "bold 603\n",
      "bona 466\n",
      "bond 2482\n",
      "bonding 752\n",
      "bonds 2202\n",
      "bone 5035\n",
      "border 191\n",
      "borderline 321\n",
      "born 258\n",
      "boston 601\n",
      "bottom 2433\n",
      "bound 9197\n",
      "boundaries 543\n",
      "boundary 313\n",
      "bouwman 390\n",
      "bovine 1973\n",
      "bowel 521\n",
      "box 4799\n",
      "boxed 221\n",
      "boxes 850\n",
      "bp 7926\n",
      "bps 289\n",
      "br 644\n",
      "brackets 220\n",
      "braf 22427\n",
      "brafv 1853\n",
      "brafwt 365\n",
      "brain 4023\n",
      "brake 400\n",
      "branch 254\n",
      "brca 82178\n",
      "brct 14851\n",
      "brd 1640\n",
      "brdu 790\n",
      "break 1082\n",
      "breakage 207\n",
      "breaker 279\n",
      "breakpoint 1353\n",
      "breakpoints 993\n",
      "breaks 1045\n",
      "breast 22743\n",
      "brg 597\n",
      "bridge 1120\n",
      "bridges 328\n",
      "bridging 236\n",
      "brief 626\n",
      "briefly 1864\n",
      "brigatinib 564\n",
      "bring 285\n",
      "brip 617\n",
      "brl 339\n",
      "broad 2291\n",
      "broader 506\n",
      "broadly 563\n",
      "bromide 392\n",
      "bromodomain 202\n",
      "bronchioloalveolar 214\n",
      "brought 274\n",
      "brown 344\n",
      "browser 271\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bs 240\n",
      "bsa 748\n",
      "bt 549\n",
      "btb 2316\n",
      "btk 675\n",
      "buffer 8558\n",
      "buffered 797\n",
      "build 336\n",
      "building 297\n",
      "built 325\n",
      "bulk 275\n",
      "bulkier 224\n",
      "bulky 504\n",
      "bundle 562\n",
      "burden 973\n",
      "buried 1839\n",
      "burkitt 734\n",
      "bxpc 419\n",
      "bypass 381\n",
      "ca 18141\n",
      "caa 272\n",
      "caax 850\n",
      "cac 413\n",
      "cadherin 1772\n",
      "cae 198\n",
      "caenorhabditis 322\n",
      "caf 221\n",
      "cag 531\n",
      "caga 192\n",
      "cah 419\n",
      "cal 222\n",
      "calbiochem 527\n",
      "calcium 1089\n",
      "calculate 851\n",
      "calculated 5759\n",
      "calculation 515\n",
      "calculations 491\n",
      "calf 1024\n",
      "california 568\n",
      "call 450\n",
      "called 1998\n",
      "calling 445\n",
      "calls 692\n",
      "calorimetry 378\n",
      "cam 208\n",
      "cambridge 541\n",
      "came 302\n",
      "camera 354\n",
      "camp 449\n",
      "can 269\n",
      "canada 449\n",
      "cance 256\n",
      "cancer 99951\n",
      "cancerdiscovery 979\n",
      "cancerous 234\n",
      "cancerres 639\n",
      "cancers 21275\n",
      "candidate 2812\n",
      "candidates 671\n",
      "cannot 4114\n",
      "canonical 2319\n",
      "cant 502\n",
      "cantly 271\n",
      "cap 281\n",
      "capabilities 198\n",
      "capability 570\n",
      "capable 1803\n",
      "capacity 2527\n",
      "capillary 395\n",
      "capture 1325\n",
      "captured 531\n",
      "carbon 422\n",
      "carbonyl 522\n",
      "carboplatin 437\n",
      "carboxy 1088\n",
      "carboxyl 1060\n",
      "carboxylate 266\n",
      "carcinogen 305\n",
      "carcinogenesis 1570\n",
      "carcinoma 9024\n",
      "carcinomas 5087\n",
      "card 1674\n",
      "cardiac 762\n",
      "cardio 249\n",
      "cardiovascular 211\n",
      "care 1201\n",
      "careful 193\n",
      "carefully 336\n",
      "caribbean 194\n",
      "carlsbad 950\n",
      "carm 490\n",
      "carma 362\n",
      "carried 5651\n",
      "carrier 1161\n",
      "carriers 3920\n",
      "carries 356\n",
      "carry 2493\n",
      "carrying 4856\n",
      "cas 250\n",
      "cascade 1559\n",
      "cascades 540\n",
      "case 12163\n",
      "casein 195\n",
      "cases 28924\n",
      "casp 323\n",
      "caspase 2896\n",
      "cassette 757\n",
      "castration 458\n",
      "cat 1483\n",
      "catalog 637\n",
      "catalogue 478\n",
      "catalysis 1204\n",
      "catalytic 12068\n",
      "catalytically 920\n",
      "catalyze 331\n",
      "catalyzed 429\n",
      "categorical 228\n",
      "categories 1123\n",
      "categorization 221\n",
      "categorized 702\n",
      "category 1036\n",
      "catenin 6852\n",
      "cation 2200\n",
      "caucasian 693\n",
      "caucasians 218\n",
      "causal 373\n",
      "causality 3082\n",
      "causally 316\n",
      "causative 737\n",
      "cause 6713\n",
      "caused 5805\n",
      "causes 2385\n",
      "causing 3065\n",
      "caution 385\n",
      "cavity 761\n",
      "cb 685\n",
      "cbf 800\n",
      "cbfa 310\n",
      "cbfb 222\n",
      "cbioportal 189\n",
      "cbl 7337\n",
      "cbp 1417\n",
      "cc 703\n",
      "cca 252\n",
      "ccc 232\n",
      "ccdc 511\n",
      "ccl 193\n",
      "ccle 303\n",
      "ccnd 2048\n",
      "ccr 416\n",
      "ccrcc 590\n",
      "cct 595\n",
      "cd 11850\n",
      "cdc 2134\n",
      "cdh 842\n",
      "cdk 16027\n",
      "cdkn 4095\n",
      "cdks 591\n",
      "cdna 10231\n",
      "cdnas 1889\n",
      "ce 196\n",
      "cebpa 310\n",
      "cef 353\n",
      "cel 278\n",
      "cell 126320\n",
      "cells 185714\n",
      "celltiter 386\n",
      "cellular 12132\n",
      "cellularity 192\n",
      "center 3849\n",
      "centered 354\n",
      "centers 656\n",
      "central 3450\n",
      "centre 495\n",
      "centrifugation 1209\n",
      "centrifuged 842\n",
      "centriole 224\n",
      "centromere 272\n",
      "centromeric 311\n",
      "centrosome 1510\n",
      "centrosomes 191\n",
      "cep 622\n",
      "cerebellar 203\n",
      "cerebellum 218\n",
      "cerebral 255\n",
      "cerevisiae 1547\n",
      "ceritinib 1873\n",
      "certain 3141\n",
      "certainly 388\n",
      "cervical 812\n",
      "cetuximab 1063\n",
      "cf 270\n",
      "cfc 2350\n",
      "cfcs 289\n",
      "cfu 369\n",
      "cg 321\n",
      "cga 230\n",
      "cgh 1008\n",
      "cgi 300\n",
      "cgnps 210\n",
      "cgp 341\n",
      "ch 523\n",
      "chain 6670\n",
      "chains 1809\n",
      "challenge 909\n",
      "challenged 203\n",
      "challenges 475\n",
      "challenging 508\n",
      "chamber 374\n",
      "chambers 198\n",
      "chan 396\n",
      "chance 540\n",
      "chang 215\n",
      "change 9980\n",
      "changed 1068\n",
      "changes 14474\n",
      "changing 374\n",
      "channel 217\n",
      "chaperone 549\n",
      "chaperones 199\n",
      "chaperonin 365\n",
      "character 211\n",
      "characterised 379\n",
      "characteristic 1902\n",
      "characteristics 4811\n",
      "characterization 3531\n",
      "characterize 1764\n",
      "characterized 8020\n",
      "characterizing 348\n",
      "charge 1941\n",
      "charged 1759\n",
      "charges 355\n",
      "charles 305\n",
      "chart 282\n",
      "chase 364\n",
      "chd 274\n",
      "check 257\n",
      "checked 561\n",
      "checkpoint 1226\n",
      "checkpoints 335\n",
      "chek 1491\n",
      "chemical 1767\n",
      "chemically 292\n",
      "chemicals 426\n",
      "chemiluminescence 606\n",
      "chemiluminescent 197\n",
      "chemistry 327\n",
      "chemokines 209\n",
      "chemotherapeutic 525\n",
      "chemotherapy 5188\n",
      "chen 937\n",
      "chest 540\n",
      "chi 316\n",
      "chicago 283\n",
      "chicken 617\n",
      "child 423\n",
      "childhood 1145\n",
      "children 2604\n",
      "chimera 409\n",
      "chimeras 343\n",
      "chimeric 1407\n",
      "china 390\n",
      "chinese 631\n",
      "chip 2037\n",
      "chk 1215\n",
      "chl 243\n",
      "chloride 276\n",
      "chloroform 271\n",
      "cho 849\n",
      "choice 455\n",
      "cholangiocarcinoma 400\n",
      "chop 200\n",
      "chose 720\n",
      "chosen 1241\n",
      "chr 595\n",
      "christopher 201\n",
      "chromatid 211\n",
      "chromatin 3559\n",
      "chromatograms 262\n",
      "chromatography 1290\n",
      "chromosomal 3979\n",
      "chromosome 7834\n",
      "chromosomes 1576\n",
      "chromothripsis 283\n",
      "chronic 3125\n",
      "chx 214\n",
      "chymotrypsin 244\n",
      "ci 4514\n",
      "cial 554\n",
      "ciap 228\n",
      "cic 1164\n",
      "ciency 441\n",
      "cient 1562\n",
      "cimp 339\n",
      "cin 311\n",
      "cip 734\n",
      "circle 449\n",
      "circles 517\n",
      "circular 436\n",
      "circulating 340\n",
      "circumstances 219\n",
      "circumvent 227\n",
      "cis 1376\n",
      "cisplatin 3816\n",
      "cited 274\n",
      "citrate 403\n",
      "city 920\n",
      "ck 692\n",
      "ckit 193\n",
      "cl 1415\n",
      "clamp 287\n",
      "clara 293\n",
      "clarified 441\n",
      "clarify 663\n",
      "clash 268\n",
      "class 10775\n",
      "classes 3409\n",
      "classic 845\n",
      "classical 2307\n",
      "classifi 2893\n",
      "classification 9859\n",
      "classifications 1061\n",
      "classified 7351\n",
      "classifier 217\n",
      "classify 1443\n",
      "classifying 365\n",
      "clear 5949\n",
      "cleared 443\n",
      "clearly 3244\n",
      "cleavage 2194\n",
      "cleaved 715\n",
      "cleft 756\n",
      "clin 848\n",
      "clincancerres 451\n",
      "clinic 1286\n",
      "clinical 34870\n",
      "clinically 4311\n",
      "clinicaltrials 237\n",
      "clinicians 628\n",
      "clinicopathologic 530\n",
      "clinicopathological 597\n",
      "clinics 269\n",
      "cll 1112\n",
      "clonal 2144\n",
      "clone 3465\n",
      "cloned 2983\n",
      "clones 6565\n",
      "cloning 1671\n",
      "clonogenic 278\n",
      "clontech 921\n",
      "close 3247\n",
      "closed 922\n",
      "closely 1546\n",
      "closer 399\n",
      "clues 275\n",
      "cluster 4030\n",
      "clustered 1426\n",
      "clustering 1870\n",
      "clusters 1484\n",
      "cm 2251\n",
      "cmet 193\n",
      "cml 2011\n",
      "cmml 773\n",
      "cmv 838\n",
      "cn 366\n",
      "cnas 250\n",
      "cns 869\n",
      "co 13315\n",
      "coa 225\n",
      "coactivator 813\n",
      "coactivators 465\n",
      "coated 609\n",
      "cocktail 549\n",
      "code 1237\n",
      "coded 333\n",
      "codes 664\n",
      "coding 6039\n",
      "codon 8635\n",
      "codons 3743\n",
      "coefficient 500\n",
      "coexisting 273\n",
      "coexpressed 810\n",
      "coexpressing 286\n",
      "coexpression 752\n",
      "cofactor 230\n",
      "cofactors 201\n",
      "cognate 385\n",
      "cohen 213\n",
      "cohesin 334\n",
      "cohort 5665\n",
      "cohorts 1768\n",
      "coil 1056\n",
      "coiled 1013\n",
      "coimmunoprecipitation 550\n",
      "coincides 219\n",
      "col 408\n",
      "cold 1190\n",
      "coli 2059\n",
      "collaboration 309\n",
      "collagen 1022\n",
      "collagenase 327\n",
      "colleagues 1445\n",
      "collected 3557\n",
      "collection 2200\n",
      "collectively 1231\n",
      "college 464\n",
      "colo 284\n",
      "colocalization 325\n",
      "colon 4098\n",
      "colonic 436\n",
      "colonies 4053\n",
      "colony 4503\n",
      "color 1375\n",
      "colorectal 6722\n",
      "colored 963\n",
      "coloured 282\n",
      "column 1188\n",
      "columns 742\n",
      "com 2567\n",
      "combination 6632\n",
      "combinations 1544\n",
      "combinatorial 299\n",
      "combine 311\n",
      "combined 5243\n",
      "combines 214\n",
      "combining 784\n",
      "come 297\n",
      "comes 393\n",
      "command 229\n",
      "commercial 381\n",
      "commercially 324\n",
      "committee 1793\n",
      "common 14350\n",
      "commonly 3602\n",
      "communication 958\n",
      "compact 267\n",
      "comparable 3837\n",
      "comparative 1440\n",
      "compare 2117\n",
      "compared 26111\n",
      "comparing 1747\n",
      "comparison 6676\n",
      "comparisons 1152\n",
      "compartment 643\n",
      "compartments 324\n",
      "compatible 643\n",
      "compelling 307\n",
      "compensate 220\n",
      "compete 392\n",
      "competent 738\n",
      "competing 246\n",
      "competition 537\n",
      "competitive 1534\n",
      "competitor 211\n",
      "compiled 319\n",
      "complement 837\n",
      "complementary 1039\n",
      "complementation 1802\n",
      "complemented 445\n",
      "complete 7454\n",
      "completed 521\n",
      "completely 3125\n",
      "completion 197\n",
      "complex 20079\n",
      "complexed 284\n",
      "complexes 6505\n",
      "complexity 699\n",
      "complicated 341\n",
      "complications 214\n",
      "component 2778\n",
      "components 3687\n",
      "composed 1518\n",
      "composite 203\n",
      "composition 623\n",
      "compound 3237\n",
      "compounds 2680\n",
      "comprehensive 2983\n",
      "comprehensively 290\n",
      "comprise 764\n",
      "comprised 1021\n",
      "comprises 699\n",
      "comprising 901\n",
      "compromise 477\n",
      "compromised 915\n",
      "computational 1669\n",
      "computed 897\n",
      "computer 333\n",
      "con 444\n",
      "conceivable 316\n",
      "conceivably 290\n",
      "concentrated 475\n",
      "concentration 6344\n",
      "concentrations 7645\n",
      "concept 712\n",
      "concerning 461\n",
      "concert 233\n",
      "conclude 1198\n",
      "concluded 692\n",
      "conclusion 2651\n",
      "conclusions 1671\n",
      "conclusive 334\n",
      "concomitant 1091\n",
      "concordance 696\n",
      "concordant 341\n",
      "concurrent 956\n",
      "concurrently 229\n",
      "condition 1185\n",
      "conditional 1399\n",
      "conditioned 402\n",
      "conditions 6997\n",
      "conduct 276\n",
      "conducted 3082\n",
      "confer 5134\n",
      "conferred 1946\n",
      "conferring 688\n",
      "confers 1402\n",
      "confi 618\n",
      "confidence 1316\n",
      "configuration 249\n",
      "confined 456\n",
      "confirm 3984\n",
      "confirmation 866\n",
      "confirmed 10661\n",
      "confirming 1634\n",
      "confirms 633\n",
      "conflicting 246\n",
      "conflicts 438\n",
      "confluence 666\n",
      "confluency 310\n",
      "confluent 459\n",
      "confocal 683\n",
      "conformation 8946\n",
      "conformational 3235\n",
      "conformations 1431\n",
      "congenital 698\n",
      "conjugated 1992\n",
      "conjugates 295\n",
      "conjugating 435\n",
      "conjunction 697\n",
      "connected 421\n",
      "connecting 252\n",
      "connection 217\n",
      "consecutive 810\n",
      "consensus 3025\n",
      "consent 1781\n",
      "consequence 1958\n",
      "consequences 4031\n",
      "consequent 403\n",
      "consequently 1382\n",
      "conservation 2515\n",
      "conservative 1278\n",
      "conserved 10989\n",
      "consider 730\n",
      "considerable 1104\n",
      "considerably 623\n",
      "consideration 646\n",
      "considerations 426\n",
      "considered 6597\n",
      "considering 1413\n",
      "consist 326\n",
      "consisted 992\n",
      "consistent 14787\n",
      "consistently 1482\n",
      "consisting 1745\n",
      "consists 1261\n",
      "consortium 855\n",
      "constant 889\n",
      "constants 279\n",
      "constitute 717\n",
      "constituted 276\n",
      "constitutes 583\n",
      "constitutional 1230\n",
      "constitutive 7196\n",
      "constitutively 4647\n",
      "constraints 280\n",
      "construct 4610\n",
      "constructed 1960\n",
      "construction 818\n",
      "constructs 8718\n",
      "contact 4534\n",
      "contacts 2466\n",
      "contain 3627\n",
      "contained 3811\n",
      "containing 18658\n",
      "contains 5982\n",
      "contaminating 209\n",
      "contamination 356\n",
      "content 1813\n",
      "context 5329\n",
      "contexts 769\n",
      "contiguous 216\n",
      "continue 402\n",
      "continued 1327\n",
      "continuous 850\n",
      "continuously 259\n",
      "contoured 196\n",
      "contrary 541\n",
      "contrast 14272\n",
      "contrasts 191\n",
      "contribute 5643\n",
      "contributed 698\n",
      "contributes 1981\n",
      "contributing 1012\n",
      "contribution 2207\n",
      "contributions 441\n",
      "control 27383\n",
      "controlled 1244\n",
      "controlling 785\n",
      "controls 9258\n",
      "controversial 395\n",
      "conventional 1411\n",
      "convergence 221\n",
      "conversely 1905\n",
      "conversion 1224\n",
      "convert 231\n",
      "converted 536\n",
      "converting 229\n",
      "converts 191\n",
      "convincing 202\n",
      "cooccurrence 245\n",
      "cooh 1448\n",
      "coomassie 462\n",
      "cooperate 699\n",
      "cooperates 280\n",
      "cooperating 311\n",
      "cooperation 424\n",
      "cooperative 715\n",
      "cooperativity 333\n",
      "coordinate 357\n",
      "coordinated 594\n",
      "coordinates 572\n",
      "coordinating 405\n",
      "coordination 758\n",
      "copies 1415\n",
      "copy 9008\n",
      "copyright 461\n",
      "cor 361\n",
      "cord 248\n",
      "core 7674\n",
      "cores 233\n",
      "corless 413\n",
      "corp 429\n",
      "corporation 257\n",
      "correct 1214\n",
      "corrected 914\n",
      "correction 662\n",
      "correctly 861\n",
      "correlate 1955\n",
      "correlated 4135\n",
      "correlates 1645\n",
      "correlating 265\n",
      "correlation 6113\n",
      "correlations 814\n",
      "correlative 196\n",
      "correspond 1902\n",
      "corresponded 468\n",
      "correspondence 397\n",
      "corresponding 8548\n",
      "corresponds 1692\n",
      "cortex 258\n",
      "cortical 240\n",
      "cos 3628\n",
      "cosegregation 1644\n",
      "cosmic 1874\n",
      "cost 218\n",
      "costello 350\n",
      "costs 286\n",
      "cotransfected 1390\n",
      "cotransfection 504\n",
      "could 21276\n",
      "coulter 359\n",
      "council 189\n",
      "counseling 1139\n",
      "count 1121\n",
      "counted 2125\n",
      "counter 404\n",
      "counteracting 237\n",
      "counterpart 447\n",
      "counterparts 535\n",
      "counterstained 346\n",
      "counting 499\n",
      "countries 452\n",
      "counts 1367\n",
      "coupled 1904\n",
      "coupling 497\n",
      "course 1572\n",
      "covalent 396\n",
      "covalently 311\n",
      "coverage 1382\n",
      "covered 433\n",
      "covering 660\n",
      "coverslips 660\n",
      "cowden 472\n",
      "cox 830\n",
      "cp 664\n",
      "cpg 1361\n",
      "cr 1926\n",
      "craf 2494\n",
      "craniofacial 258\n",
      "craniosynostosis 301\n",
      "crc 2608\n",
      "crcs 726\n",
      "cre 3328\n",
      "create 1039\n",
      "created 1403\n",
      "creates 373\n",
      "creating 450\n",
      "creation 216\n",
      "creb 493\n",
      "crebbp 644\n",
      "creert 1874\n",
      "crenolanib 1922\n",
      "crest 442\n",
      "crisis 226\n",
      "criteria 5556\n",
      "criterion 381\n",
      "critical 7886\n",
      "critically 314\n",
      "crizotinib 8511\n",
      "crl 804\n",
      "crm 251\n",
      "crook 193\n",
      "cross 3022\n",
      "crossed 373\n",
      "crossref 518\n",
      "crosstalk 281\n",
      "crpc 604\n",
      "crt 610\n",
      "crucial 1807\n",
      "cruz 3401\n",
      "cryptic 892\n",
      "crystal 5731\n",
      "crystallization 402\n",
      "crystallized 214\n",
      "crystallographic 1067\n",
      "crystallography 361\n",
      "crystals 1003\n",
      "cs 1322\n",
      "csd 364\n",
      "csf 1367\n",
      "csl 229\n",
      "cst 247\n",
      "ct 2228\n",
      "ctbp 189\n",
      "ctc 252\n",
      "ctcf 875\n",
      "ctd 2513\n",
      "cter 243\n",
      "ctg 333\n",
      "ctip 332\n",
      "ctl 196\n",
      "ctla 435\n",
      "ctnnb 1318\n",
      "ctrl 216\n",
      "ctt 206\n",
      "cul 4735\n",
      "cullin 842\n",
      "culture 6457\n",
      "cultured 4881\n",
      "cultures 1802\n",
      "cumulative 543\n",
      "curated 258\n",
      "curation 286\n",
      "curative 261\n",
      "current 4107\n",
      "currently 3776\n",
      "curve 1380\n",
      "curves 1459\n",
      "custom 428\n",
      "cut 816\n",
      "cutaneous 1440\n",
      "cutoff 681\n",
      "cwr 323\n",
      "cx 203\n",
      "cxcl 248\n",
      "cy 460\n",
      "cyan 511\n",
      "cyck 846\n",
      "cycle 10199\n",
      "cycles 1887\n",
      "cyclin 13174\n",
      "cycling 506\n",
      "cyclins 668\n",
      "cycloheximide 607\n",
      "cys 2698\n",
      "cysteine 2195\n",
      "cysteines 308\n",
      "cystic 258\n",
      "cyt 466\n",
      "cytogenetic 1459\n",
      "cytogenetics 236\n",
      "cytokeratin 294\n",
      "cytokine 2006\n",
      "cytokines 1145\n",
      "cytometer 259\n",
      "cytometric 447\n",
      "cytometry 1777\n",
      "cytoplasm 2034\n",
      "cytoplasmic 3582\n",
      "cytosine 466\n",
      "cytoskeletal 393\n",
      "cytoskeleton 415\n",
      "cytosol 513\n",
      "cytosolic 928\n",
      "cytotoxic 870\n",
      "cytotoxicity 376\n",
      "da 469\n",
      "dab 412\n",
      "dabrafenib 715\n",
      "dacomitinib 236\n",
      "daily 2227\n",
      "dako 635\n",
      "damage 5077\n",
      "damaged 452\n",
      "damaging 1913\n",
      "dana 386\n",
      "dang 202\n",
      "danvers 405\n",
      "dapi 622\n",
      "dark 581\n",
      "dasatinib 3474\n",
      "dashed 583\n",
      "data 60429\n",
      "database 6753\n",
      "databases 1092\n",
      "dataset 1646\n",
      "datasets 594\n",
      "date 3380\n",
      "daughter 214\n",
      "david 205\n",
      "davies 319\n",
      "daxx 767\n",
      "day 5776\n",
      "days 7719\n",
      "db 217\n",
      "dbd 1822\n",
      "dbp 324\n",
      "dbsnp 493\n",
      "dc 685\n",
      "dcaf 1182\n",
      "dcr 255\n",
      "dcs 236\n",
      "dd 2746\n",
      "ddit 300\n",
      "ddr 2775\n",
      "de 4436\n",
      "dead 1664\n",
      "death 4274\n",
      "deaths 686\n",
      "debris 288\n",
      "decade 354\n",
      "decades 535\n",
      "decay 312\n",
      "december 720\n",
      "decided 717\n",
      "decision 498\n",
      "decisions 700\n",
      "declaration 299\n",
      "decline 195\n",
      "decrease 4027\n",
      "decreased 7027\n",
      "decreases 836\n",
      "decreasing 540\n",
      "dedicated 214\n",
      "deduced 203\n",
      "deemed 257\n",
      "deep 903\n",
      "deeper 199\n",
      "default 350\n",
      "defect 3431\n",
      "defective 4623\n",
      "defects 5282\n",
      "defense 285\n",
      "defi 1539\n",
      "deficiency 1529\n",
      "deficient 4527\n",
      "define 2778\n",
      "defined 7710\n",
      "defines 381\n",
      "defining 798\n",
      "definite 340\n",
      "definitely 195\n",
      "definition 853\n",
      "definitive 483\n",
      "definitively 193\n",
      "defrayed 219\n",
      "degradation 6182\n",
      "degraded 723\n",
      "degree 3262\n",
      "degrees 723\n",
      "degron 363\n",
      "dehydrogenase 664\n",
      "del 8373\n",
      "delag 625\n",
      "delay 651\n",
      "delayed 539\n",
      "delc 410\n",
      "dele 532\n",
      "delete 198\n",
      "deleted 2549\n",
      "deleterious 17083\n",
      "deleting 212\n",
      "deletion 16654\n",
      "deletions 9342\n",
      "delg 214\n",
      "delineate 243\n",
      "delivery 238\n",
      "dell 333\n",
      "delt 327\n",
      "delta 686\n",
      "demethylase 300\n",
      "demethylation 305\n",
      "demetri 375\n",
      "demographic 342\n",
      "demographics 211\n",
      "demonstrate 4878\n",
      "demonstrated 10549\n",
      "demonstrates 1246\n",
      "demonstrating 1846\n",
      "demonstration 470\n",
      "denaturation 800\n",
      "denatured 707\n",
      "denaturing 947\n",
      "dendritic 234\n",
      "denote 289\n",
      "denoted 415\n",
      "denotes 353\n",
      "densities 232\n",
      "densitometric 262\n",
      "densitometry 677\n",
      "density 2844\n",
      "deoxycholate 254\n",
      "department 2129\n",
      "depend 963\n",
      "dependence 1213\n",
      "dependency 747\n",
      "dependent 18463\n",
      "depending 1598\n",
      "depends 1122\n",
      "dephosphorylate 429\n",
      "dephosphorylated 327\n",
      "dephosphorylation 972\n",
      "depicted 1120\n",
      "depicting 222\n",
      "depiction 194\n",
      "depleted 1188\n",
      "depletion 2659\n",
      "deposited 602\n",
      "deprivation 727\n",
      "deprived 384\n",
      "depth 656\n",
      "deptor 566\n",
      "der 1204\n",
      "deregulated 1213\n",
      "deregulation 1045\n",
      "derivative 559\n",
      "derivatives 598\n",
      "derive 511\n",
      "derived 12927\n",
      "describe 1263\n",
      "described 27423\n",
      "describes 279\n",
      "describing 405\n",
      "description 1557\n",
      "design 1864\n",
      "designated 798\n",
      "designation 244\n",
      "designed 2542\n",
      "desired 246\n",
      "despite 5301\n",
      "destabilization 877\n",
      "destabilize 1366\n",
      "destabilized 1142\n",
      "destabilizes 363\n",
      "destabilizing 1031\n",
      "destruction 557\n",
      "detail 1462\n",
      "detailed 3118\n",
      "details 1616\n",
      "detect 3754\n",
      "detectable 3212\n",
      "detected 17901\n",
      "detecting 515\n",
      "detection 5314\n",
      "determinant 546\n",
      "determinants 411\n",
      "determination 1379\n",
      "determine 9863\n",
      "determined 12944\n",
      "determines 305\n",
      "determining 1010\n",
      "detrimental 329\n",
      "develop 3769\n",
      "developed 6878\n",
      "developing 2068\n",
      "development 14382\n",
      "developmental 2333\n",
      "develops 230\n",
      "deviation 1798\n",
      "deviations 607\n",
      "devoid 261\n",
      "dexamethasone 248\n",
      "dextran 239\n",
      "df 283\n",
      "dfg 2213\n",
      "dfs 276\n",
      "dgc 229\n",
      "dh 223\n",
      "dharmacon 197\n",
      "dhl 291\n",
      "dhplc 387\n",
      "dht 852\n",
      "di 1102\n",
      "diabetes 423\n",
      "diagnosed 3185\n",
      "diagnoses 348\n",
      "diagnosis 5731\n",
      "diagnostic 1918\n",
      "diagnostics 542\n",
      "diagram 878\n",
      "diameter 762\n",
      "diarrhea 198\n",
      "dic 334\n",
      "dicer 1476\n",
      "dickinson 385\n",
      "die 378\n",
      "died 1204\n",
      "diego 728\n",
      "diehl 256\n",
      "dif 211\n",
      "differ 1507\n",
      "differed 790\n",
      "difference 6000\n",
      "differences 8851\n",
      "different 30353\n",
      "differential 3048\n",
      "differentially 2017\n",
      "differentiate 401\n",
      "differentiated 1708\n",
      "differentiation 6248\n",
      "differently 529\n",
      "differing 468\n",
      "differs 552\n",
      "difficult 2138\n",
      "difficulties 470\n",
      "difficulty 292\n",
      "diffraction 242\n",
      "diffuse 1271\n",
      "digested 1287\n",
      "digestion 2125\n",
      "digital 442\n",
      "dikk 448\n",
      "diluted 1316\n",
      "dilution 1795\n",
      "dilutions 552\n",
      "dimensional 1924\n",
      "dimer 2464\n",
      "dimeric 630\n",
      "dimerization 4236\n",
      "dimerize 274\n",
      "dimers 1458\n",
      "dimethyl 366\n",
      "diminish 483\n",
      "diminished 1410\n",
      "dinucleotide 861\n",
      "dinucleotides 264\n",
      "dipg 272\n",
      "dipgs 213\n",
      "diploid 540\n",
      "direct 7839\n",
      "directed 5015\n",
      "direction 471\n",
      "directions 607\n",
      "directly 7619\n",
      "disability 236\n",
      "disclosed 241\n",
      "disclosure 195\n",
      "discontinuation 296\n",
      "discontinued 315\n",
      "discordant 546\n",
      "discover 287\n",
      "discovered 2054\n",
      "discovery 4126\n",
      "discrepancies 361\n",
      "discrepancy 589\n",
      "discrete 459\n",
      "discriminate 381\n",
      "discuss 472\n",
      "discussed 1361\n",
      "discussion 6521\n",
      "disease 26329\n",
      "diseases 2277\n",
      "dish 674\n",
      "dishes 893\n",
      "disorder 1586\n",
      "disordered 680\n",
      "disorders 2812\n",
      "dispensable 471\n",
      "displacement 284\n",
      "display 3026\n",
      "displayed 5404\n",
      "displaying 943\n",
      "displays 951\n",
      "disrupt 3687\n",
      "disrupted 1494\n",
      "disrupting 1018\n",
      "disruption 2592\n",
      "disruptive 400\n",
      "disrupts 1077\n",
      "dissect 292\n",
      "dissected 307\n",
      "dissection 470\n",
      "dissociation 1813\n",
      "dissolved 429\n",
      "distal 1059\n",
      "distance 1138\n",
      "distances 219\n",
      "distant 863\n",
      "distinct 9355\n",
      "distinction 255\n",
      "distinctive 508\n",
      "distinguish 965\n",
      "distinguished 556\n",
      "distinguishing 326\n",
      "distortion 221\n",
      "distributed 1040\n",
      "distribution 5297\n",
      "distributions 633\n",
      "disulfide 790\n",
      "dithiothreitol 370\n",
      "divergent 538\n",
      "diverse 3195\n",
      "diversity 1171\n",
      "divided 1099\n",
      "dividing 304\n",
      "division 1020\n",
      "dl 522\n",
      "dlbcl 2883\n",
      "dlbcls 359\n",
      "dld 408\n",
      "dlg 549\n",
      "dll 544\n",
      "dm 248\n",
      "dmem 2361\n",
      "dmso 1510\n",
      "dn 1375\n",
      "dna 56314\n",
      "dnas 752\n",
      "dnase 234\n",
      "dneasy 195\n",
      "dnmt 1631\n",
      "do 194\n",
      "docetaxel 206\n",
      "docking 1063\n",
      "document 214\n",
      "documented 2667\n",
      "dod 1007\n",
      "dodecyl 438\n",
      "doi 2504\n",
      "domain 65544\n",
      "domains 19539\n",
      "dominant 5242\n",
      "done 3285\n",
      "donor 1380\n",
      "donors 287\n",
      "dosage 753\n",
      "dose 6356\n",
      "doses 2132\n",
      "dosing 514\n",
      "dot 382\n",
      "dots 231\n",
      "dotted 341\n",
      "double 5393\n",
      "doublet 202\n",
      "doubling 192\n",
      "dovitinib 1143\n",
      "down 3227\n",
      "download 7372\n",
      "downloaded 2880\n",
      "downregulate 275\n",
      "downregulated 945\n",
      "downregulation 1408\n",
      "downstream 10851\n",
      "dox 749\n",
      "doxorubicin 430\n",
      "doxycycline 877\n",
      "dp 345\n",
      "dpc 690\n",
      "dr 2943\n",
      "dramatic 1288\n",
      "dramatically 1054\n",
      "drastic 206\n",
      "drastically 197\n",
      "draw 215\n",
      "drawn 675\n",
      "dried 480\n",
      "drive 1519\n",
      "driven 4635\n",
      "driver 4557\n",
      "drivers 1141\n",
      "drives 581\n",
      "driving 1004\n",
      "drop 427\n",
      "drosophila 1498\n",
      "drug 12347\n",
      "druggable 278\n",
      "drugs 3685\n",
      "dry 234\n",
      "ds 265\n",
      "dsb 600\n",
      "dsbh 569\n",
      "dsbs 683\n",
      "dsmz 208\n",
      "dsred 946\n",
      "dssp 199\n",
      "dt 345\n",
      "dtnfa 352\n",
      "dtt 962\n",
      "du 322\n",
      "dual 2291\n",
      "duct 267\n",
      "ductal 774\n",
      "due 11721\n",
      "dulbecco 1023\n",
      "duodenal 195\n",
      "dup 679\n",
      "duplex 388\n",
      "duplexes 216\n",
      "duplicate 848\n",
      "duplicated 322\n",
      "duplicates 225\n",
      "duplication 2078\n",
      "duplications 843\n",
      "durable 612\n",
      "duration 1449\n",
      "dusp 328\n",
      "dutch 200\n",
      "dux 222\n",
      "dye 696\n",
      "dynamic 1331\n",
      "dynamical 190\n",
      "dynamics 1367\n",
      "dysfunction 430\n",
      "dysplasia 884\n",
      "dysplastic 325\n",
      "dysregulated 570\n",
      "dysregulation 811\n",
      "eagle 870\n",
      "earlier 1997\n",
      "early 6956\n",
      "ease 237\n",
      "easily 800\n",
      "east 475\n",
      "eastern 445\n",
      "easton 335\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "easy 343\n",
      "eb 1080\n",
      "ebc 293\n",
      "ebna 194\n",
      "ebp 897\n",
      "ebraf 666\n",
      "ebv 709\n",
      "ec 1267\n",
      "ecd 871\n",
      "ecl 489\n",
      "ecm 252\n",
      "ecog 312\n",
      "ecori 951\n",
      "ecorv 206\n",
      "ect 235\n",
      "ectodomain 367\n",
      "ectopic 1685\n",
      "ectopically 678\n",
      "ed 2777\n",
      "edge 217\n",
      "edited 289\n",
      "edta 2012\n",
      "edu 861\n",
      "eec 198\n",
      "eecs 537\n",
      "ef 790\n",
      "effect 22700\n",
      "effective 5140\n",
      "effectively 2030\n",
      "effectiveness 803\n",
      "effector 3233\n",
      "effectors 2384\n",
      "effects 19116\n",
      "effi 268\n",
      "efficacious 252\n",
      "efficacy 4327\n",
      "efficiencies 405\n",
      "efficiency 2407\n",
      "efficient 1595\n",
      "efficiently 1758\n",
      "effort 1255\n",
      "efforts 1739\n",
      "effusion 537\n",
      "effusions 325\n",
      "efs 223\n",
      "eg 364\n",
      "egf 4552\n",
      "egfp 2434\n",
      "egfr 54831\n",
      "egfrs 397\n",
      "egfrviii 1260\n",
      "egta 297\n",
      "egy 272\n",
      "eight 5507\n",
      "eighteen 256\n",
      "eighty 251\n",
      "either 20146\n",
      "ek 238\n",
      "el 296\n",
      "electron 889\n",
      "electrophoresed 422\n",
      "electrophoresis 1860\n",
      "electrophoretic 464\n",
      "electroporation 376\n",
      "electrostatic 819\n",
      "elegans 582\n",
      "element 1265\n",
      "elements 2534\n",
      "elevate 209\n",
      "elevated 4566\n",
      "elevation 493\n",
      "eleven 555\n",
      "elf 2758\n",
      "elicit 309\n",
      "elicited 200\n",
      "elicits 204\n",
      "eligibility 209\n",
      "eligible 461\n",
      "eliminate 460\n",
      "eliminated 541\n",
      "eliminating 332\n",
      "elimination 266\n",
      "elisa 468\n",
      "elk 475\n",
      "elmer 451\n",
      "elongated 286\n",
      "elongation 568\n",
      "elongin 868\n",
      "elonginc 232\n",
      "elsewhere 800\n",
      "elucidate 1060\n",
      "elucidated 664\n",
      "elucidating 192\n",
      "elucidation 217\n",
      "elusive 319\n",
      "eluted 1040\n",
      "elution 391\n",
      "embedded 2580\n",
      "embryo 622\n",
      "embryogenesis 282\n",
      "embryonic 5524\n",
      "embryos 1451\n",
      "emd 517\n",
      "emerge 447\n",
      "emerged 957\n",
      "emergence 1020\n",
      "emerges 216\n",
      "emerging 983\n",
      "emission 600\n",
      "eml 2473\n",
      "emphasize 348\n",
      "emphasizing 280\n",
      "employed 1406\n",
      "employing 314\n",
      "empty 3871\n",
      "ems 370\n",
      "emsa 511\n",
      "emt 764\n",
      "en 1116\n",
      "enable 605\n",
      "enabled 426\n",
      "enables 501\n",
      "enabling 369\n",
      "encode 1371\n",
      "encoded 2835\n",
      "encodes 2612\n",
      "encoding 6854\n",
      "encompass 328\n",
      "encompasses 200\n",
      "encompassing 1037\n",
      "encountered 240\n",
      "encourage 201\n",
      "encouraging 275\n",
      "encyclopedia 231\n",
      "end 5341\n",
      "endocrine 618\n",
      "endocytosis 214\n",
      "endogenous 8482\n",
      "endometrial 3836\n",
      "endometrioid 886\n",
      "endometrium 279\n",
      "endonuclease 990\n",
      "endothelial 1381\n",
      "ends 961\n",
      "endt 193\n",
      "energy 1357\n",
      "enforced 343\n",
      "engage 268\n",
      "engaged 238\n",
      "engagement 232\n",
      "engages 194\n",
      "engineered 1597\n",
      "england 664\n",
      "enhance 1535\n",
      "enhanced 6192\n",
      "enhancement 621\n",
      "enhancer 1193\n",
      "enhancers 483\n",
      "enhances 1177\n",
      "enhancing 478\n",
      "enlarged 328\n",
      "enough 714\n",
      "enrich 196\n",
      "enriched 2727\n",
      "enrichment 2065\n",
      "enrolled 1015\n",
      "enrollment 265\n",
      "ensembl 301\n",
      "ensemble 230\n",
      "ensure 880\n",
      "ensures 198\n",
      "enter 363\n",
      "entered 309\n",
      "entire 2851\n",
      "entirely 532\n",
      "entities 324\n",
      "entity 262\n",
      "entrectinib 558\n",
      "entries 325\n",
      "entropy 263\n",
      "entry 1089\n",
      "environment 741\n",
      "environmental 494\n",
      "enzalutamide 336\n",
      "enzymatic 2279\n",
      "enzymatically 271\n",
      "enzyme 5513\n",
      "enzymes 2205\n",
      "eosin 644\n",
      "eosinophilia 404\n",
      "eosinophilic 267\n",
      "eosinophils 191\n",
      "eot 253\n",
      "ep 685\n",
      "epas 544\n",
      "epcam 253\n",
      "epha 216\n",
      "epidermal 2703\n",
      "epigenetic 1926\n",
      "epilepsy 249\n",
      "epithelia 202\n",
      "epithelial 4496\n",
      "epithelioid 400\n",
      "epithelium 815\n",
      "epitope 1889\n",
      "epn 233\n",
      "epo 359\n",
      "epor 484\n",
      "equal 2764\n",
      "equally 1003\n",
      "equation 412\n",
      "equilibrated 260\n",
      "equilibrium 1196\n",
      "equipped 313\n",
      "equivalent 2862\n",
      "er 6583\n",
      "era 220\n",
      "erbb 18401\n",
      "ercc 3182\n",
      "ere 260\n",
      "erg 2579\n",
      "erge 256\n",
      "erk 18171\n",
      "erlotinib 6772\n",
      "error 2585\n",
      "errors 507\n",
      "ers 212\n",
      "erythrocytosis 194\n",
      "erythroid 763\n",
      "erythropoietin 287\n",
      "es 2401\n",
      "escalation 236\n",
      "escape 676\n",
      "escc 447\n",
      "escherichia 618\n",
      "ese 376\n",
      "esophageal 679\n",
      "especially 2600\n",
      "esr 863\n",
      "essential 5699\n",
      "essentially 1083\n",
      "establish 1397\n",
      "established 5771\n",
      "establishing 408\n",
      "establishment 352\n",
      "estimate 1677\n",
      "estimated 3839\n",
      "estimates 992\n",
      "estimation 198\n",
      "estradiol 509\n",
      "estrogen 1984\n",
      "et 104496\n",
      "etc 491\n",
      "etge 612\n",
      "ethanol 812\n",
      "ethical 253\n",
      "ethics 640\n",
      "ethidium 240\n",
      "ethnic 1098\n",
      "ethnicity 638\n",
      "etiology 517\n",
      "eto 501\n",
      "etoposide 303\n",
      "ets 2800\n",
      "etv 4657\n",
      "eukaryotes 379\n",
      "eukaryotic 662\n",
      "europe 290\n",
      "european 1033\n",
      "ev 802\n",
      "evade 204\n",
      "evaluable 490\n",
      "evaluate 2745\n",
      "evaluated 5522\n",
      "evaluating 539\n",
      "evaluation 3304\n",
      "evaluations 205\n",
      "even 7622\n",
      "event 3237\n",
      "events 6009\n",
      "eventually 878\n",
      "ever 327\n",
      "everolimus 504\n",
      "every 2581\n",
      "evi 959\n",
      "evidence 13053\n",
      "evidenced 772\n",
      "evident 1457\n",
      "evolution 1109\n",
      "evolutionarily 914\n",
      "evolutionary 1363\n",
      "evolve 275\n",
      "evolved 314\n",
      "evolving 201\n",
      "evs 445\n",
      "ewing 879\n",
      "ews 1712\n",
      "ewsr 1209\n",
      "ex 2448\n",
      "exact 2360\n",
      "exactly 290\n",
      "examination 2011\n",
      "examine 2894\n",
      "examined 11005\n",
      "examining 789\n",
      "example 7715\n",
      "examples 1689\n",
      "exceed 229\n",
      "exceeded 398\n",
      "excel 317\n",
      "excellent 559\n",
      "except 3377\n",
      "exception 1961\n",
      "exceptional 219\n",
      "exceptions 375\n",
      "excess 1194\n",
      "excessive 339\n",
      "exchange 2314\n",
      "excised 454\n",
      "excision 618\n",
      "excitation 245\n",
      "exclude 1566\n",
      "excluded 2146\n",
      "excluding 691\n",
      "exclusion 841\n",
      "exclusive 1862\n",
      "exclusively 1537\n",
      "exclusivity 204\n",
      "exel 563\n",
      "exemplified 226\n",
      "exert 619\n",
      "exerted 335\n",
      "exerts 458\n",
      "exhibit 3117\n",
      "exhibited 5194\n",
      "exhibiting 651\n",
      "exhibits 963\n",
      "exist 1896\n",
      "existed 362\n",
      "existence 1043\n",
      "existing 840\n",
      "exists 1136\n",
      "exit 208\n",
      "exo 1259\n",
      "exogenous 1931\n",
      "exogenously 315\n",
      "exome 3404\n",
      "exomes 369\n",
      "exon 39483\n",
      "exonic 1178\n",
      "exons 11379\n",
      "exonuclease 848\n",
      "expand 434\n",
      "expanded 821\n",
      "expanding 314\n",
      "expansion 1129\n",
      "expect 392\n",
      "expected 7633\n",
      "experience 942\n",
      "experienced 986\n",
      "experiment 3144\n",
      "experimental 5418\n",
      "experimentally 771\n",
      "experiments 14396\n",
      "expert 204\n",
      "expertise 231\n",
      "explain 2502\n",
      "explained 1484\n",
      "explaining 698\n",
      "explains 298\n",
      "explanation 1820\n",
      "explanations 271\n",
      "exploited 367\n",
      "explore 1239\n",
      "explored 958\n",
      "exponential 210\n",
      "export 955\n",
      "exposed 3830\n",
      "exposure 2929\n",
      "express 4265\n",
      "expressed 19498\n",
      "expresses 531\n",
      "expressing 24484\n",
      "expression 77580\n",
      "expressions 251\n",
      "extend 798\n",
      "extended 2066\n",
      "extending 350\n",
      "extends 394\n",
      "extension 1352\n",
      "extensive 2119\n",
      "extensively 910\n",
      "extent 2445\n",
      "external 690\n",
      "extra 593\n",
      "extracellular 5052\n",
      "extract 1100\n",
      "extracted 3676\n",
      "extraction 1703\n",
      "extracts 2982\n",
      "extreme 688\n",
      "extremely 1147\n",
      "eye 504\n",
      "ezh 3232\n",
      "ezr 206\n",
      "fa 2029\n",
      "fab 245\n",
      "fabian 209\n",
      "face 1307\n",
      "facial 607\n",
      "facilitate 1985\n",
      "facilitated 450\n",
      "facilitates 746\n",
      "facilitating 413\n",
      "facility 292\n",
      "facio 234\n",
      "facs 870\n",
      "facscalibur 203\n",
      "fact 5671\n",
      "factor 16115\n",
      "factors 10432\n",
      "fadd 215\n",
      "fail 951\n",
      "failed 3717\n",
      "fails 531\n",
      "failure 2479\n",
      "fak 585\n",
      "fall 888\n",
      "falling 389\n",
      "falls 445\n",
      "false 1253\n",
      "fam 684\n",
      "familial 3393\n",
      "families 8853\n",
      "family 21168\n",
      "fanca 1447\n",
      "fancb 254\n",
      "fancc 509\n",
      "fancd 701\n",
      "fancg 616\n",
      "fancj 458\n",
      "fancl 523\n",
      "fanconi 438\n",
      "fap 234\n",
      "far 3746\n",
      "farber 382\n",
      "farrugia 189\n",
      "fas 405\n",
      "fashion 1129\n",
      "fast 672\n",
      "faster 587\n",
      "fat 2243\n",
      "fatal 244\n",
      "fate 582\n",
      "father 470\n",
      "fatigue 256\n",
      "favor 2467\n",
      "favorable 1253\n",
      "favored 207\n",
      "favoring 198\n",
      "favors 233\n",
      "favour 210\n",
      "fax 528\n",
      "fbs 2288\n",
      "fbw 2844\n",
      "fbxw 2367\n",
      "fc 740\n",
      "fcs 1152\n",
      "fda 567\n",
      "fdg 214\n",
      "fdr 548\n",
      "fe 448\n",
      "feasible 285\n",
      "feature 2086\n",
      "features 7034\n",
      "february 396\n",
      "fedratinib 1012\n",
      "feedback 1299\n",
      "fell 453\n",
      "female 2552\n",
      "females 1193\n",
      "ferm 670\n",
      "fetal 2427\n",
      "fewer 1038\n",
      "ffpe 910\n",
      "fgf 4497\n",
      "fgfr 24553\n",
      "fgfrs 742\n",
      "fgfs 333\n",
      "fh 1777\n",
      "fha 363\n",
      "fhl 241\n",
      "fi 766\n",
      "fibroblast 1191\n",
      "fibroblasts 2845\n",
      "fibronectin 357\n",
      "fibrosarcoma 198\n",
      "fibrosis 328\n",
      "fibrous 351\n",
      "fide 475\n",
      "fidelity 424\n",
      "field 783\n",
      "fields 351\n",
      "fifteen 275\n",
      "fifth 302\n",
      "fifty 321\n",
      "fig 104481\n",
      "figs 2864\n",
      "figure 102657\n",
      "figureopen 2965\n",
      "figures 6501\n",
      "file 976\n",
      "files 590\n",
      "filled 439\n",
      "fills 233\n",
      "film 315\n",
      "filter 700\n",
      "filtered 493\n",
      "filtering 309\n",
      "filters 333\n",
      "filtration 994\n",
      "final 3454\n",
      "finally 5318\n",
      "find 2059\n",
      "finding 4345\n",
      "findings 9899\n",
      "fine 488\n",
      "finger 1424\n",
      "fingers 269\n",
      "finnish 189\n",
      "fip 828\n",
      "firefly 686\n",
      "first 19161\n",
      "fish 4275\n",
      "fisher 1888\n",
      "fit 609\n",
      "fitc 457\n",
      "fitness 289\n",
      "fitted 346\n",
      "fitting 362\n",
      "five 9784\n",
      "fixation 209\n",
      "fixed 3440\n",
      "fkbp 532\n",
      "fl 3471\n",
      "flag 7635\n",
      "flank 400\n",
      "flanked 337\n",
      "flanking 1707\n",
      "flash 317\n",
      "flat 251\n",
      "fletcher 264\n",
      "flex 2078\n",
      "flexibility 754\n",
      "flexible 716\n",
      "fli 1318\n",
      "flies 192\n",
      "flip 199\n",
      "flow 2965\n",
      "flox 328\n",
      "flp 567\n",
      "flt 16988\n",
      "fluid 237\n",
      "fluor 302\n",
      "fluorescein 278\n",
      "fluorescence 3746\n",
      "fluorescent 1790\n",
      "fluorescently 208\n",
      "fluoride 887\n",
      "fm 698\n",
      "fms 237\n",
      "fmtc 425\n",
      "fniii 377\n",
      "fo 539\n",
      "focal 2016\n",
      "foci 3695\n",
      "focus 2598\n",
      "focused 1827\n",
      "focusing 325\n",
      "fold 12523\n",
      "folded 1031\n",
      "folding 2583\n",
      "folds 214\n",
      "follicular 722\n",
      "follow 2144\n",
      "followed 6726\n",
      "following 11650\n",
      "follows 2051\n",
      "food 360\n",
      "fop 567\n",
      "force 292\n",
      "forced 288\n",
      "foretinib 490\n",
      "forkhead 195\n",
      "form 11441\n",
      "formaldehyde 370\n",
      "formalin 1479\n",
      "formally 316\n",
      "formamide 197\n",
      "format 446\n",
      "formation 13137\n",
      "formed 4036\n",
      "former 1214\n",
      "forming 1513\n",
      "forms 6597\n",
      "formula 351\n",
      "forty 800\n",
      "forward 2475\n",
      "fos 437\n",
      "foster 625\n",
      "found 37568\n",
      "foundation 533\n",
      "founder 1002\n",
      "four 15376\n",
      "fourteen 239\n",
      "fourth 720\n",
      "foxa 1487\n",
      "foxl 360\n",
      "foxo 1012\n",
      "foxp 362\n",
      "fp 245\n",
      "fpd 463\n",
      "fr 653\n",
      "fraction 4275\n",
      "fractionated 273\n",
      "fractionation 399\n",
      "fractions 1358\n",
      "fragment 3891\n",
      "fragmented 195\n",
      "fragments 2810\n",
      "frame 5605\n",
      "frameshift 2679\n",
      "framework 598\n",
      "france 842\n",
      "francisco 398\n",
      "fraumeni 445\n",
      "free 7849\n",
      "french 623\n",
      "frequencies 3027\n",
      "frequency 12591\n",
      "frequent 4915\n",
      "frequently 6925\n",
      "fresh 956\n",
      "freshly 241\n",
      "frk 637\n",
      "front 335\n",
      "frozen 2312\n",
      "frs 545\n",
      "frt 214\n",
      "fs 1908\n",
      "fsrc 1048\n",
      "ft 239\n",
      "ftis 211\n",
      "fu 216\n",
      "fubp 232\n",
      "fugene 702\n",
      "fulfilled 317\n",
      "full 12560\n",
      "fully 3777\n",
      "fulvestrant 428\n",
      "function 37388\n",
      "functional 33412\n",
      "functionality 448\n",
      "functionally 4026\n",
      "functioning 370\n",
      "functions 7687\n",
      "fundamental 610\n",
      "furin 410\n",
      "furthermore 8328\n",
      "fus 288\n",
      "fused 2119\n",
      "fusion 18207\n",
      "fusions 6148\n",
      "future 2817\n",
      "fw 209\n",
      "fzr 280\n",
      "ga 648\n",
      "gaa 278\n",
      "gab 444\n",
      "gac 357\n",
      "gadd 334\n",
      "gag 375\n",
      "gain 7569\n",
      "gained 398\n",
      "gains 1087\n",
      "gal 2881\n",
      "galactose 1043\n",
      "galactosidase 1035\n",
      "gallus 357\n",
      "gamma 706\n",
      "gap 4875\n",
      "gapdh 939\n",
      "gaps 941\n",
      "gas 249\n",
      "gastric 3014\n",
      "gastrointestinal 2054\n",
      "gat 230\n",
      "gata 2187\n",
      "gatekeeper 1874\n",
      "gavage 242\n",
      "gave 985\n",
      "gbc 288\n",
      "gbm 2659\n",
      "gbms 906\n",
      "gc 1684\n",
      "gca 206\n",
      "gcb 676\n",
      "gct 227\n",
      "gd 1141\n",
      "gdc 1115\n",
      "gdna 202\n",
      "gdnf 280\n",
      "gdp 2532\n",
      "ge 758\n",
      "gef 935\n",
      "gefitinib 15422\n",
      "gefs 591\n",
      "gel 5426\n",
      "gels 1724\n",
      "gemcitabine 521\n",
      "genbank 924\n",
      "gender 766\n",
      "gene 61440\n",
      "genechip 204\n",
      "genentech 275\n",
      "general 4306\n",
      "generalized 224\n",
      "generally 2885\n",
      "generate 3142\n",
      "generated 8843\n",
      "generates 390\n",
      "generating 799\n",
      "generation 4618\n",
      "generations 447\n",
      "generic 216\n",
      "generous 299\n",
      "genes 41036\n",
      "genesis 214\n",
      "genet 314\n",
      "genetic 18970\n",
      "genetically 1267\n",
      "geneticin 189\n",
      "genetics 2415\n",
      "genome 8063\n",
      "genomes 1271\n",
      "genomic 14057\n",
      "genomics 772\n",
      "genotoxic 399\n",
      "genotype 2727\n",
      "genotyped 467\n",
      "genotypes 870\n",
      "genotyping 1210\n",
      "geo 260\n",
      "geographic 327\n",
      "george 531\n",
      "germ 2596\n",
      "german 299\n",
      "germany 2007\n",
      "germinal 415\n",
      "germline 10138\n",
      "get 194\n",
      "gfp 9445\n",
      "gg 379\n",
      "gga 300\n",
      "ggc 234\n",
      "ggg 213\n",
      "ggt 349\n",
      "gi 1161\n",
      "giant 199\n",
      "gibco 699\n",
      "giemsa 205\n",
      "gift 1160\n",
      "gist 6616\n",
      "gists 5327\n",
      "give 1072\n",
      "given 9025\n",
      "gives 408\n",
      "giving 395\n",
      "gk 635\n",
      "gl 206\n",
      "gland 647\n",
      "glands 384\n",
      "glass 386\n",
      "gleevec 420\n",
      "gli 1399\n",
      "glial 338\n",
      "glioblastoma 2074\n",
      "glioblastomas 688\n",
      "glioma 1407\n",
      "gliomas 1746\n",
      "gln 2313\n",
      "glo 442\n",
      "global 1963\n",
      "glover 250\n",
      "glu 4284\n",
      "glucose 1629\n",
      "glut 400\n",
      "glutamate 441\n",
      "glutamic 595\n",
      "glutamine 1326\n",
      "glutathione 1257\n",
      "gly 4293\n",
      "glycerol 941\n",
      "glycerophosphate 199\n",
      "glycine 1726\n",
      "glycogen 340\n",
      "glycolytic 225\n",
      "glycosylated 353\n",
      "glycosylation 388\n",
      "gm 1120\n",
      "gmbh 197\n",
      "gna 263\n",
      "gnaq 293\n",
      "gnas 530\n",
      "go 9289\n",
      "goal 599\n",
      "goat 1037\n",
      "gof 372\n",
      "gold 440\n",
      "goldgar 266\n",
      "goldstein 265\n",
      "golgi 412\n",
      "good 1881\n",
      "google 703\n",
      "gorlin 289\n",
      "gov 1207\n",
      "gp 278\n",
      "gppnhp 363\n",
      "gq 633\n",
      "gr 708\n",
      "grade 4389\n",
      "graded 394\n",
      "grades 300\n",
      "gradient 596\n",
      "grand 239\n",
      "grant 427\n",
      "grantham 913\n",
      "granulocyte 329\n",
      "granulosa 206\n",
      "graph 707\n",
      "graphical 191\n",
      "graphpad 597\n",
      "graphs 270\n",
      "gray 1267\n",
      "grb 568\n",
      "grd 1006\n",
      "great 864\n",
      "greater 5289\n",
      "greatest 600\n",
      "greatly 1170\n",
      "green 5075\n",
      "grew 730\n",
      "grey 249\n",
      "groove 1275\n",
      "gross 451\n",
      "group 15469\n",
      "grouped 364\n",
      "groups 6491\n",
      "grow 1034\n",
      "growing 1417\n",
      "grown 3496\n",
      "growth 37975\n",
      "gs 1577\n",
      "gse 403\n",
      "gsea 429\n",
      "gsi 624\n",
      "gsk 4256\n",
      "gst 4728\n",
      "gt 366\n",
      "gtg 354\n",
      "gtl 513\n",
      "gtp 7816\n",
      "gtpase 3169\n",
      "gtpases 651\n",
      "gtt 195\n",
      "guanidinium 222\n",
      "guanine 1011\n",
      "guanosine 265\n",
      "guest 304\n",
      "guide 569\n",
      "guided 458\n",
      "guidelines 1612\n",
      "gv 907\n",
      "gvgd 1571\n",
      "gy 446\n",
      "ha 6764\n",
      "haematological 272\n",
      "haematopoietic 420\n",
      "hair 297\n",
      "hairpin 803\n",
      "half 4212\n",
      "hall 192\n",
      "hallmark 575\n",
      "hallmarks 404\n",
      "hamartin 2125\n",
      "hamartoma 325\n",
      "hampered 213\n",
      "hamster 393\n",
      "hand 2836\n",
      "hands 218\n",
      "haploinsufficiency 638\n",
      "haploinsufficient 315\n",
      "haplotype 661\n",
      "haplotypes 567\n",
      "harbor 3087\n",
      "harbored 2693\n",
      "harboring 6556\n",
      "harbors 662\n",
      "harbour 475\n",
      "harboured 220\n",
      "harbouring 606\n",
      "harvard 427\n",
      "harvested 2704\n",
      "harvesting 254\n",
      "hat 561\n",
      "hazard 422\n",
      "hazards 214\n",
      "hbc 353\n",
      "hboc 402\n",
      "hbrca 1170\n",
      "hc 213\n",
      "hca 979\n",
      "hcc 5122\n",
      "hccs 611\n",
      "hcdc 313\n",
      "hcf 201\n",
      "hcl 1889\n",
      "hcm 235\n",
      "hct 2176\n",
      "hd 2086\n",
      "hdac 1007\n",
      "hdf 216\n",
      "hdr 1827\n",
      "hdx 322\n",
      "head 1949\n",
      "healing 324\n",
      "health 1163\n",
      "healthcare 593\n",
      "healthy 1425\n",
      "heart 722\n",
      "heat 2190\n",
      "heated 223\n",
      "heavily 380\n",
      "heavy 570\n",
      "hec 336\n",
      "hedgehog 387\n",
      "heidelberg 235\n",
      "height 423\n",
      "heinrich 783\n",
      "hek 5249\n",
      "hel 349\n",
      "hela 2732\n",
      "held 241\n",
      "helical 2228\n",
      "helicase 1184\n",
      "helices 1930\n",
      "helix 8526\n",
      "help 3268\n",
      "helpful 487\n",
      "helps 206\n",
      "helsinki 395\n",
      "hemagglutinin 392\n",
      "hematologic 1053\n",
      "hematological 317\n",
      "hematology 398\n",
      "hematopoiesis 603\n",
      "hematopoietic 3000\n",
      "hematoxylin 799\n",
      "hemizygous 411\n",
      "hemoglobin 192\n",
      "hence 2194\n",
      "henikoff 239\n",
      "hep 404\n",
      "heparan 239\n",
      "heparin 382\n",
      "hepatic 395\n",
      "hepatitis 203\n",
      "hepatocellular 982\n",
      "hepatocyte 458\n",
      "hepatocytes 431\n",
      "hepes 1036\n",
      "hepg 256\n",
      "her 639\n",
      "hercules 393\n",
      "hereafter 421\n",
      "hereby 249\n",
      "hereditary 1993\n",
      "herein 633\n",
      "hes 1742\n",
      "hetero 498\n",
      "heterocomplex 624\n",
      "heterodimer 2445\n",
      "heterodimeric 371\n",
      "heterodimerization 1180\n",
      "heterodimers 989\n",
      "heteroduplex 354\n",
      "heterogeneity 2325\n",
      "heterogeneous 1553\n",
      "heterologous 1105\n",
      "heterotrimer 314\n",
      "heterotrimeric 528\n",
      "heterozygosity 1534\n",
      "heterozygote 258\n",
      "heterozygotes 510\n",
      "heterozygous 4918\n",
      "hey 496\n",
      "hf 301\n",
      "hg 3184\n",
      "hgf 1736\n",
      "hgg 286\n",
      "hggs 376\n",
      "hgmd 281\n",
      "hgs 655\n",
      "hgvs 264\n",
      "hh 1723\n",
      "hi 302\n",
      "hierarchical 546\n",
      "hies 285\n",
      "hif 6934\n",
      "high 27064\n",
      "higher 13209\n",
      "highest 2533\n",
      "highlight 974\n",
      "highlighted 1347\n",
      "highlighting 445\n",
      "highlights 950\n",
      "highly 11635\n",
      "hilden 280\n",
      "hill 265\n",
      "hindiii 604\n",
      "hindrance 290\n",
      "hinge 1495\n",
      "hippel 209\n",
      "hippo 352\n",
      "hirschsprung 236\n",
      "his 737\n",
      "hiseq 426\n",
      "histidine 944\n",
      "histiocytic 335\n",
      "histogram 460\n",
      "histologic 1242\n",
      "histological 1327\n",
      "histologically 662\n",
      "histology 1993\n",
      "histone 3982\n",
      "histones 346\n",
      "histopathologic 429\n",
      "histopathological 463\n",
      "histopathology 755\n",
      "histories 597\n",
      "history 7765\n",
      "hit 555\n",
      "hits 459\n",
      "hiv 221\n",
      "hl 308\n",
      "hla 1741\n",
      "hlh 242\n",
      "hmc 976\n",
      "hmg 861\n",
      "hmler 190\n",
      "hmlh 1095\n",
      "hmsh 1115\n",
      "hmuts 279\n",
      "hn 364\n",
      "hnf 1097\n",
      "hnpcc 1201\n",
      "hnscc 1430\n",
      "ho 220\n",
      "hoc 286\n",
      "hodgkin 456\n",
      "holds 443\n",
      "holoenzyme 1777\n",
      "holoenzymes 206\n",
      "homeobox 198\n",
      "homeostasis 489\n",
      "homo 1095\n",
      "homodimer 468\n",
      "homodimerization 345\n",
      "homodimers 665\n",
      "homogeneity 200\n",
      "homogeneous 374\n",
      "homolog 1278\n",
      "homologous 3440\n",
      "homologs 568\n",
      "homologue 445\n",
      "homologues 256\n",
      "homology 3197\n",
      "homotrimer 625\n",
      "homozygosity 394\n",
      "homozygotes 208\n",
      "homozygous 3870\n",
      "hoogeveen 408\n",
      "hook 485\n",
      "hope 254\n",
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      "hormone 1995\n",
      "hormones 201\n",
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      "hsnf 348\n",
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      "ht 861\n",
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      "html 347\n",
      "http 5181\n",
      "https 213\n",
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      "human 38486\n",
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      "hundred 711\n",
      "hundreds 233\n",
      "hybrid 1471\n",
      "hybridization 2519\n",
      "hybridized 588\n",
      "hydrogen 4049\n",
      "hydrolysis 2829\n",
      "hydrophilic 216\n",
      "hydrophobic 4738\n",
      "hydrophobicity 284\n",
      "hydroxyl 433\n",
      "hydroxylated 221\n",
      "hydroxylation 337\n",
      "hyg 198\n",
      "hygromycin 240\n",
      "hyper 546\n",
      "hyperactivating 331\n",
      "hyperactivation 718\n",
      "hyperactive 500\n",
      "hypermethylated 221\n",
      "hypermethylation 835\n",
      "hypermutated 577\n",
      "hyperphosphorylated 250\n",
      "hyperplasia 625\n",
      "hypersensitive 402\n",
      "hypersensitivity 453\n",
      "hypochondroplasia 245\n",
      "hypomorphic 484\n",
      "hypotheses 326\n",
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      "hypothesize 544\n",
      "hypothesized 1499\n",
      "hypoxia 950\n",
      "hypoxic 321\n",
      "ia 505\n",
      "iab 264\n",
      "iarc 2349\n",
      "ib 532\n",
      "ic 8828\n",
      "icca 336\n",
      "icd 237\n",
      "ice 1589\n",
      "icgc 226\n",
      "icl 369\n",
      "icn 611\n",
      "id 2615\n",
      "idea 1231\n",
      "ideal 505\n",
      "ideally 266\n",
      "identi 218\n",
      "identical 3524\n",
      "identifi 681\n",
      "identification 5749\n",
      "identified 38233\n",
      "identifies 676\n",
      "identify 8367\n",
      "identifying 1541\n",
      "identities 230\n",
      "identity 1414\n",
      "idh 8775\n",
      "ids 272\n",
      "ie 396\n",
      "ifdi 320\n",
      "ifdis 384\n",
      "ifitm 368\n",
      "ifn 978\n",
      "ig 1227\n",
      "ige 248\n",
      "igf 1575\n",
      "igg 1480\n",
      "igh 440\n",
      "igm 255\n",
      "ihc 1796\n",
      "ii 12201\n",
      "iii 5240\n",
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      "il 9366\n",
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      "illustrate 713\n",
      "illustrated 992\n",
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      "illustration 391\n",
      "im 832\n",
      "image 6354\n",
      "imaged 191\n",
      "imagej 309\n",
      "imagequant 259\n",
      "images 1816\n",
      "imaging 1267\n",
      "imatinib 18585\n",
      "imbalance 283\n",
      "imidazole 316\n",
      "immature 778\n",
      "immediate 474\n",
      "immediately 1213\n",
      "immobilized 606\n",
      "immobilon 268\n",
      "immortalization 202\n",
      "immortalized 836\n",
      "immune 2065\n",
      "immunity 261\n",
      "immunoblot 2769\n",
      "immunoblots 692\n",
      "immunoblotted 1024\n",
      "immunoblotting 3606\n",
      "immunodeficiency 258\n",
      "immunofluorescence 1594\n",
      "immunofluorescent 317\n",
      "immunoglobulin 909\n",
      "immunohistochemical 1731\n",
      "immunohistochemistry 2752\n",
      "immunoprecipitate 306\n",
      "immunoprecipitated 3141\n",
      "immunoprecipitates 1515\n",
      "immunoprecipitation 3704\n",
      "immunoprecipitations 471\n",
      "immunoreactivity 355\n",
      "immunostained 203\n",
      "immunostaining 842\n",
      "immunotherapy 236\n",
      "impact 6991\n",
      "impacts 403\n",
      "impair 1436\n",
      "impaired 4741\n",
      "impairing 253\n",
      "impairment 767\n",
      "impairs 513\n",
      "impede 206\n",
      "implantation 205\n",
      "implanted 537\n",
      "implementation 506\n",
      "implemented 660\n",
      "implicate 392\n",
      "implicated 3340\n",
      "implicating 384\n",
      "implication 257\n",
      "implications 2228\n",
      "implied 211\n",
      "implies 551\n",
      "imply 599\n",
      "implying 599\n",
      "import 365\n",
      "importance 3882\n",
      "important 15606\n",
      "importantly 3618\n",
      "imposed 241\n",
      "improve 1022\n",
      "improved 1428\n",
      "improvement 626\n",
      "improving 260\n",
      "imt 209\n",
      "in 5621\n",
      "inability 831\n",
      "inactivate 1103\n",
      "inactivated 1552\n",
      "inactivates 365\n",
      "inactivating 1925\n",
      "inactivation 5413\n",
      "inactive 6751\n",
      "inappropriate 300\n",
      "inc 3445\n",
      "incidence 3547\n",
      "include 5879\n",
      "included 9295\n",
      "includes 2545\n",
      "including 24588\n",
      "inclusion 1250\n",
      "inclusions 309\n",
      "incomplete 766\n",
      "incompletely 251\n",
      "inconclusive 271\n",
      "inconsistent 275\n",
      "incorporate 469\n",
      "incorporated 660\n",
      "incorporates 197\n",
      "incorporating 432\n",
      "incorporation 1141\n",
      "increase 11600\n",
      "increased 23192\n",
      "increases 2933\n",
      "increasing 3807\n",
      "increasingly 574\n",
      "incubated 7603\n",
      "incubating 470\n",
      "incubation 2295\n",
      "incubator 295\n",
      "indeed 5800\n",
      "indel 350\n",
      "indels 1016\n",
      "independence 764\n",
      "independent 18889\n",
      "independently 2123\n",
      "index 3762\n",
      "indianapolis 263\n",
      "indicate 9672\n",
      "indicated 17887\n",
      "indicates 5286\n",
      "indicating 7395\n",
      "indication 367\n",
      "indications 310\n",
      "indicative 706\n",
      "indicator 517\n",
      "indirect 994\n",
      "indirectly 694\n",
      "indispensable 238\n",
      "indistinguishable 925\n",
      "individual 8607\n",
      "individually 555\n",
      "individuals 9773\n",
      "indolent 282\n",
      "induce 7772\n",
      "induced 19192\n",
      "induces 3012\n",
      "inducibility 232\n",
      "inducible 2163\n",
      "inducing 1627\n",
      "induction 5627\n",
      "ineffective 442\n",
      "infants 210\n",
      "infect 198\n",
      "infected 2688\n",
      "infection 2114\n",
      "infections 566\n",
      "infer 205\n",
      "inferior 223\n",
      "inferred 294\n",
      "infiltrating 427\n",
      "infiltration 499\n",
      "inflammation 700\n",
      "inflammatory 1884\n",
      "influence 2729\n",
      "influenced 509\n",
      "influences 421\n",
      "influencing 209\n",
      "inform 286\n",
      "information 11120\n",
      "informative 838\n",
      "informed 1658\n",
      "inframe 267\n",
      "infrequent 361\n",
      "infrequently 261\n",
      "inherent 378\n",
      "inheritance 528\n",
      "inherited 1759\n",
      "inhibit 6353\n",
      "inhibited 6823\n",
      "inhibiting 2160\n",
      "inhibition 19580\n",
      "inhibitor 20056\n",
      "inhibitors 21755\n",
      "inhibitory 4267\n",
      "inhibits 3661\n",
      "ini 502\n",
      "initial 4489\n",
      "initially 2119\n",
      "initiate 691\n",
      "initiated 1508\n",
      "initiates 257\n",
      "initiating 819\n",
      "initiation 2745\n",
      "initiative 220\n",
      "injected 2439\n",
      "injection 1265\n",
      "ink 7365\n",
      "inline 279\n",
      "inlineview 1141\n",
      "inner 252\n",
      "inoculated 286\n",
      "inositol 765\n",
      "inpp 430\n",
      "input 1089\n",
      "ins 2450\n",
      "insc 939\n",
      "insect 636\n",
      "insensitive 1022\n",
      "insensitivity 242\n",
      "insert 1413\n",
      "inserted 1280\n",
      "insertion 5651\n",
      "insertions 4428\n",
      "inserts 372\n",
      "inset 262\n",
      "insfqea 794\n",
      "inside 225\n",
      "insight 3828\n",
      "insights 1616\n",
      "insnpg 582\n",
      "insoluble 198\n",
      "inspected 202\n",
      "inspection 316\n",
      "inss 512\n",
      "instability 3178\n",
      "instance 954\n",
      "instances 441\n",
      "instead 1710\n",
      "institute 2888\n",
      "institutes 330\n",
      "institution 284\n",
      "institutional 1886\n",
      "institutions 275\n",
      "instructions 2771\n",
      "instrument 330\n",
      "instrumental 297\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "insufficient 854\n",
      "insulin 2163\n",
      "int 207\n",
      "intact 2408\n",
      "integral 281\n",
      "integrated 1853\n",
      "integration 1124\n",
      "integrations 228\n",
      "integrative 901\n",
      "integrin 529\n",
      "integrins 211\n",
      "integrity 1727\n",
      "intense 414\n",
      "intensities 659\n",
      "intensity 2334\n",
      "intensive 489\n",
      "inter 747\n",
      "interact 4876\n",
      "interacted 386\n",
      "interacting 2405\n",
      "interaction 20159\n",
      "interactions 11604\n",
      "interacts 3184\n",
      "interchromosomal 193\n",
      "interdomain 407\n",
      "interest 2843\n",
      "interesting 1770\n",
      "interestingly 7982\n",
      "interface 4767\n",
      "interfaces 426\n",
      "interfere 1235\n",
      "interference 1139\n",
      "interferes 313\n",
      "interfering 826\n",
      "interferon 372\n",
      "interleukin 866\n",
      "intermediate 5018\n",
      "intermediates 317\n",
      "intermolecular 522\n",
      "internal 2456\n",
      "internalization 438\n",
      "international 1205\n",
      "interphase 443\n",
      "interplay 244\n",
      "interpret 285\n",
      "interpretation 1948\n",
      "interpreted 484\n",
      "interpreting 257\n",
      "interrogate 218\n",
      "interrogated 215\n",
      "interstitial 532\n",
      "interval 1082\n",
      "intervals 538\n",
      "intervening 298\n",
      "intervention 747\n",
      "interventions 190\n",
      "intestinal 958\n",
      "intestine 679\n",
      "intolerant 546\n",
      "intra 629\n",
      "intracellular 2946\n",
      "intracranial 191\n",
      "intragenic 637\n",
      "intramolecular 789\n",
      "intriguing 842\n",
      "intriguingly 760\n",
      "intrinsic 3532\n",
      "intrinsically 283\n",
      "introduce 750\n",
      "introduced 3301\n",
      "introduces 230\n",
      "introducing 454\n",
      "introduction 6222\n",
      "intron 2949\n",
      "intronic 1539\n",
      "introns 636\n",
      "inv 355\n",
      "invariably 338\n",
      "invariant 922\n",
      "invasion 2764\n",
      "invasive 2135\n",
      "invasiveness 318\n",
      "inverse 342\n",
      "inversely 243\n",
      "inversion 198\n",
      "inversions 203\n",
      "inverted 248\n",
      "investigate 3768\n",
      "investigated 5036\n",
      "investigating 380\n",
      "investigation 1899\n",
      "investigations 646\n",
      "investigators 566\n",
      "invitrogen 4468\n",
      "involve 1816\n",
      "involved 11088\n",
      "involvement 1999\n",
      "involves 1562\n",
      "involving 5272\n",
      "iodide 459\n",
      "ion 841\n",
      "ionizing 488\n",
      "ions 347\n",
      "ip 1789\n",
      "iptg 256\n",
      "iqr 230\n",
      "ir 1103\n",
      "irb 366\n",
      "ires 510\n",
      "iressa 262\n",
      "irf 233\n",
      "iron 454\n",
      "irradiated 594\n",
      "irradiation 763\n",
      "irrespective 562\n",
      "irreversible 943\n",
      "irs 729\n",
      "ish 1251\n",
      "ishikawa 256\n",
      "island 392\n",
      "islands 205\n",
      "isocitrate 797\n",
      "isoform 2802\n",
      "isoforms 3824\n",
      "isogenic 663\n",
      "isolate 279\n",
      "isolated 5327\n",
      "isolation 968\n",
      "isoleucine 584\n",
      "isopropyl 198\n",
      "isothermal 223\n",
      "isothiocyanate 236\n",
      "issue 1475\n",
      "issues 676\n",
      "italic 321\n",
      "italics 191\n",
      "italy 300\n",
      "itd 5305\n",
      "itds 302\n",
      "iv 3060\n",
      "ivs 2982\n",
      "jackson 341\n",
      "jade 424\n",
      "jag 268\n",
      "jagged 223\n",
      "jak 13794\n",
      "janoueix 209\n",
      "january 629\n",
      "janus 221\n",
      "japan 2014\n",
      "japanese 887\n",
      "jc 311\n",
      "jco 201\n",
      "jeg 195\n",
      "jewish 314\n",
      "jh 1214\n",
      "jhuem 274\n",
      "jm 2769\n",
      "jmml 611\n",
      "jmr 495\n",
      "jnj 524\n",
      "jnk 1433\n",
      "johnson 338\n",
      "joined 189\n",
      "joining 295\n",
      "joint 355\n",
      "jolla 647\n",
      "jones 239\n",
      "jose 217\n",
      "journal 965\n",
      "jp 266\n",
      "jpg 386\n",
      "judged 471\n",
      "july 1431\n",
      "jun 1258\n",
      "junction 1072\n",
      "junctions 981\n",
      "june 1535\n",
      "jurkat 431\n",
      "juvenile 422\n",
      "juxtamembrane 2283\n",
      "ka 315\n",
      "kaplan 1488\n",
      "karpas 282\n",
      "karyotype 567\n",
      "karyotypes 201\n",
      "kb 4728\n",
      "kcal 623\n",
      "kcat 721\n",
      "kcl 466\n",
      "kconfab 470\n",
      "kd 5817\n",
      "kda 3018\n",
      "kdm 1528\n",
      "kdr 651\n",
      "ke 368\n",
      "keap 5374\n",
      "keeping 496\n",
      "kelch 463\n",
      "kept 345\n",
      "keratinocytes 374\n",
      "ketoglutarate 407\n",
      "kettering 940\n",
      "key 6016\n",
      "keywords 1533\n",
      "kg 2606\n",
      "ki 2359\n",
      "kiaa 592\n",
      "kidney 1856\n",
      "kif 552\n",
      "kim 458\n",
      "kinase 65718\n",
      "kinases 10308\n",
      "kind 712\n",
      "kindly 804\n",
      "kindred 237\n",
      "kindreds 443\n",
      "kinds 240\n",
      "kinetic 661\n",
      "kinetics 843\n",
      "kingdom 451\n",
      "kip 807\n",
      "kirsten 223\n",
      "kit 37615\n",
      "kitd 709\n",
      "kits 255\n",
      "kitv 535\n",
      "kitwt 378\n",
      "kix 192\n",
      "km 1166\n",
      "kms 212\n",
      "kmt 2774\n",
      "knock 1308\n",
      "knockdown 4599\n",
      "knocked 402\n",
      "knockout 1282\n",
      "know 376\n",
      "knowledge 2305\n",
      "known 20377\n",
      "ko 694\n",
      "kodak 198\n",
      "koh 273\n",
      "korea 208\n",
      "kp 371\n",
      "kras 13959\n",
      "krasd 194\n",
      "krasex 224\n",
      "krasg 2964\n",
      "kraslsl 475\n",
      "ks 870\n",
      "ksr 319\n",
      "kts 365\n",
      "ku 297\n",
      "la 1072\n",
      "lab 449\n",
      "label 332\n",
      "labeled 3844\n",
      "labeling 725\n",
      "labelled 588\n",
      "labelling 227\n",
      "laboratories 3221\n",
      "laboratory 1996\n",
      "lac 398\n",
      "lack 5867\n",
      "lacked 999\n",
      "lacking 3244\n",
      "lacks 1236\n",
      "lacz 1036\n",
      "laemmli 297\n",
      "lake 340\n",
      "landscape 984\n",
      "lane 4694\n",
      "lanes 4051\n",
      "langerhans 190\n",
      "lapatinib 4183\n",
      "large 14440\n",
      "largedownload 637\n",
      "largely 2093\n",
      "larger 4702\n",
      "largest 958\n",
      "laser 524\n",
      "last 2391\n",
      "lasting 211\n",
      "lastly 338\n",
      "late 1334\n",
      "latency 854\n",
      "latent 246\n",
      "later 3028\n",
      "lats 968\n",
      "latter 2372\n",
      "lau 410\n",
      "layer 690\n",
      "layers 200\n",
      "lb 421\n",
      "lbd 650\n",
      "lbh 237\n",
      "lbp 308\n",
      "lc 866\n",
      "lch 1077\n",
      "le 189\n",
      "lead 6074\n",
      "leading 5792\n",
      "leads 5139\n",
      "learning 265\n",
      "least 10883\n",
      "leaves 244\n",
      "leaving 447\n",
      "led 4827\n",
      "lee 1688\n",
      "lef 445\n",
      "left 4557\n",
      "legend 1792\n",
      "leica 342\n",
      "leiden 356\n",
      "leiomyomas 307\n",
      "length 8599\n",
      "lengths 534\n",
      "lentiviral 1230\n",
      "lentivirus 441\n",
      "lentiviruses 339\n",
      "leopard 217\n",
      "lerosey 209\n",
      "lesion 1299\n",
      "lesions 5004\n",
      "less 11265\n",
      "lesser 806\n",
      "lestaurtinib 392\n",
      "let 364\n",
      "lethal 1183\n",
      "lethality 807\n",
      "letters 226\n",
      "leu 4454\n",
      "leucine 1305\n",
      "leukaemia 1101\n",
      "leukaemias 211\n",
      "leukemia 8917\n",
      "leukemias 1035\n",
      "leukemic 1310\n",
      "leukemogenesis 613\n",
      "leukemogenic 266\n",
      "leukocyte 260\n",
      "leukocytes 330\n",
      "leukocytosis 271\n",
      "leupeptin 404\n",
      "level 14718\n",
      "levels 34180\n",
      "lexa 565\n",
      "lfs 562\n",
      "lg 268\n",
      "lgs 256\n",
      "li 2081\n",
      "libraries 944\n",
      "library 1839\n",
      "lie 597\n",
      "lies 818\n",
      "life 4055\n",
      "lifetime 479\n",
      "ligand 10412\n",
      "ligands 2336\n",
      "ligase 4900\n",
      "ligases 787\n",
      "ligated 635\n",
      "ligating 239\n",
      "ligation 847\n",
      "light 2477\n",
      "like 10507\n",
      "likelihood 3655\n",
      "likely 18507\n",
      "likewise 1170\n",
      "lim 310\n",
      "limb 235\n",
      "limit 712\n",
      "limitation 448\n",
      "limitations 626\n",
      "limited 4985\n",
      "limiting 775\n",
      "limits 1317\n",
      "lin 527\n",
      "lindau 211\n",
      "line 23398\n",
      "lineage 1968\n",
      "lineages 668\n",
      "linear 808\n",
      "lines 33876\n",
      "link 1308\n",
      "linkage 1071\n",
      "linked 4523\n",
      "linker 2330\n",
      "linking 1057\n",
      "links 314\n",
      "lipid 3069\n",
      "lipids 339\n",
      "lipofectamine 1364\n",
      "liquid 1589\n",
      "liss 189\n",
      "list 1280\n",
      "listed 2448\n",
      "lists 326\n",
      "liter 193\n",
      "literature 2280\n",
      "littermates 300\n",
      "little 3179\n",
      "liu 405\n",
      "live 417\n",
      "lived 203\n",
      "liver 3892\n",
      "livers 201\n",
      "living 337\n",
      "lk 281\n",
      "lkb 2713\n",
      "llc 193\n",
      "llr 512\n",
      "lm 538\n",
      "lmo 219\n",
      "lmvs 380\n",
      "ln 494\n",
      "lncap 1575\n",
      "lnr 197\n",
      "lo 209\n",
      "load 202\n",
      "loaded 1086\n",
      "loading 1953\n",
      "lobe 2119\n",
      "lobes 389\n",
      "lobular 214\n",
      "local 2042\n",
      "localization 6311\n",
      "localize 481\n",
      "localized 2665\n",
      "localizes 371\n",
      "locally 506\n",
      "located 9815\n",
      "location 3137\n",
      "locations 1043\n",
      "loci 1570\n",
      "locus 4863\n",
      "lodged 196\n",
      "log 3160\n",
      "logistic 1050\n",
      "loh 3785\n",
      "london 294\n",
      "long 4083\n",
      "longer 2915\n",
      "longest 217\n",
      "look 506\n",
      "looked 315\n",
      "looking 214\n",
      "loop 17057\n",
      "loops 2964\n",
      "lorlatinib 284\n",
      "lose 346\n",
      "loss 24077\n",
      "losses 676\n",
      "lost 2236\n",
      "louis 813\n",
      "lovd 569\n",
      "low 15567\n",
      "lower 10031\n",
      "lowest 548\n",
      "lox 329\n",
      "loxp 439\n",
      "lp 292\n",
      "lr 1147\n",
      "lraf 221\n",
      "lrp 237\n",
      "lrs 223\n",
      "ls 1732\n",
      "lsdb 200\n",
      "lsk 269\n",
      "lsl 961\n",
      "lt 231\n",
      "ltd 212\n",
      "ltr 216\n",
      "ltrs 214\n",
      "lu 749\n",
      "luc 1483\n",
      "luciferase 6266\n",
      "luminal 618\n",
      "luminescence 262\n",
      "luminometer 231\n",
      "lung 21101\n",
      "lungs 320\n",
      "lux 234\n",
      "lv 286\n",
      "ly 1947\n",
      "lymph 1602\n",
      "lymphadenopathy 258\n",
      "lymphatic 208\n",
      "lymphoblastic 1162\n",
      "lymphoblastoid 531\n",
      "lymphocyte 474\n",
      "lymphocytes 1204\n",
      "lymphocytic 546\n",
      "lymphoid 1188\n",
      "lymphoma 4490\n",
      "lymphomagenesis 275\n",
      "lymphomas 1437\n",
      "lyn 208\n",
      "lynch 949\n",
      "lys 4276\n",
      "lysate 1704\n",
      "lysates 7368\n",
      "lysed 2911\n",
      "lysine 1868\n",
      "lysis 2774\n",
      "lysosomal 198\n",
      "ma 440\n",
      "mab 1057\n",
      "mac 341\n",
      "machine 203\n",
      "machinery 1035\n",
      "macrocephaly 377\n",
      "macroh 370\n",
      "macrophage 452\n",
      "macrophages 382\n",
      "mad 270\n",
      "made 4055\n",
      "madison 698\n",
      "maf 203\n",
      "magenta 398\n",
      "magnesium 193\n",
      "magnetic 468\n",
      "magnification 898\n",
      "magnitude 718\n",
      "mail 797\n",
      "main 2128\n",
      "mainly 2501\n",
      "maintain 1184\n",
      "maintained 2661\n",
      "maintaining 1045\n",
      "maintains 423\n",
      "maintenance 1473\n",
      "major 6684\n",
      "majority 6712\n",
      "make 1918\n",
      "makes 1507\n",
      "making 1357\n",
      "maldi 207\n",
      "male 2378\n",
      "males 637\n",
      "malformations 236\n",
      "malignancies 3677\n",
      "malignancy 1482\n",
      "malignant 5267\n",
      "malt 309\n",
      "mam 228\n",
      "maml 558\n",
      "mammalian 5663\n",
      "mammals 701\n",
      "mammary 2278\n",
      "man 359\n",
      "management 1145\n",
      "manassas 301\n",
      "mandatory 190\n",
      "manifest 261\n",
      "manifestation 199\n",
      "manifestations 437\n",
      "manifested 290\n",
      "manipulation 239\n",
      "mann 491\n",
      "manner 4740\n",
      "mannheim 359\n",
      "manning 220\n",
      "manual 361\n",
      "manually 581\n",
      "manufacturer 4575\n",
      "manuscript 6171\n",
      "many 12186\n",
      "map 6741\n",
      "mapk 8370\n",
      "mapp 197\n",
      "mapped 2048\n",
      "mapping 1922\n",
      "maps 871\n",
      "marais 195\n",
      "march 571\n",
      "marginal 239\n",
      "marginally 374\n",
      "mark 1153\n",
      "marked 3528\n",
      "markedly 2140\n",
      "marker 2628\n",
      "markers 2977\n",
      "marks 566\n",
      "marrow 3664\n",
      "mass 2847\n",
      "massachusetts 388\n",
      "masses 206\n",
      "massive 291\n",
      "massively 415\n",
      "mast 981\n",
      "master 528\n",
      "mastocytosis 1041\n",
      "match 335\n",
      "matched 2728\n",
      "matching 390\n",
      "material 2976\n",
      "materials 5443\n",
      "maternal 253\n",
      "math 1675\n",
      "mathjax 583\n",
      "mathml 544\n",
      "matrigel 743\n",
      "matrix 1640\n",
      "matter 222\n",
      "maturation 632\n",
      "mature 1147\n",
      "max 816\n",
      "maximal 1179\n",
      "maximum 1247\n",
      "may 49302\n",
      "mayo 280\n",
      "mb 2417\n",
      "mbp 256\n",
      "mbrca 234\n",
      "mc 2712\n",
      "mcc 259\n",
      "mcf 5185\n",
      "mcherry 790\n",
      "mcl 970\n",
      "mcmahon 221\n",
      "mcrpc 376\n",
      "mct 232\n",
      "md 1608\n",
      "mda 931\n",
      "mdc 375\n",
      "mdm 2216\n",
      "mdmx 218\n",
      "mds 3409\n",
      "mdv 275\n",
      "mean 8137\n",
      "meaningful 300\n",
      "means 2406\n",
      "measurable 611\n",
      "measure 2140\n",
      "measured 7478\n",
      "measurement 908\n",
      "measurements 1541\n",
      "measures 675\n",
      "measuring 841\n",
      "mechanism 11930\n",
      "mechanisms 11250\n",
      "mechanistic 1054\n",
      "mechanistically 416\n",
      "med 1162\n",
      "media 2467\n",
      "median 6510\n",
      "mediastinal 223\n",
      "mediate 2199\n",
      "mediated 14316\n",
      "mediates 1495\n",
      "mediating 1196\n",
      "mediator 835\n",
      "mediators 479\n",
      "medical 2598\n",
      "medicine 1656\n",
      "medium 8241\n",
      "medullary 335\n",
      "medulloblastoma 552\n",
      "medulloblastomas 251\n",
      "meet 272\n",
      "meeting 723\n",
      "meetings 260\n",
      "mef 2908\n",
      "mefs 2377\n",
      "megakaryocytes 238\n",
      "meier 1446\n",
      "mek 18269\n",
      "meki 356\n",
      "mekis 282\n",
      "mel 1620\n",
      "melan 283\n",
      "melanocyte 339\n",
      "melanocytes 884\n",
      "melanocytic 369\n",
      "melanogaster 397\n",
      "melanoma 16181\n",
      "melanomas 5117\n",
      "melting 404\n",
      "mem 335\n",
      "member 1886\n",
      "members 6115\n",
      "membrane 8671\n",
      "membranes 2670\n",
      "memorial 1008\n",
      "memory 405\n",
      "men 2386\n",
      "mendelian 295\n",
      "meningiomas 216\n",
      "mental 462\n",
      "mentioned 888\n",
      "mer 304\n",
      "mercaptoethanol 376\n",
      "merged 224\n",
      "merlin 2771\n",
      "mesenchymal 1269\n",
      "meso 192\n",
      "mesothelioma 405\n",
      "messenger 507\n",
      "mesylate 1225\n",
      "met 17560\n",
      "meta 494\n",
      "metabolic 1060\n",
      "metabolism 1109\n",
      "metabolite 354\n",
      "metal 435\n",
      "metalloproteases 204\n",
      "metaphase 435\n",
      "metaphases 219\n",
      "metastases 2442\n",
      "metastasis 3709\n",
      "metastatic 5546\n",
      "metex 343\n",
      "methanol 551\n",
      "methionine 1426\n",
      "method 6071\n",
      "methodology 456\n",
      "methods 15074\n",
      "methyl 1284\n",
      "methylated 965\n",
      "methylation 4895\n",
      "methylcellulose 330\n",
      "methyltransferase 616\n",
      "methyltransferases 213\n",
      "mf 338\n",
      "mg 11073\n",
      "mga 197\n",
      "mgcl 1117\n",
      "mgh 632\n",
      "mgmt 215\n",
      "mh 2986\n",
      "mhc 280\n",
      "mi 330\n",
      "mice 24055\n",
      "michael 472\n",
      "micro 251\n",
      "microarray 1745\n",
      "microarrays 453\n",
      "microattribution 931\n",
      "microdeletions 227\n",
      "microdissected 289\n",
      "microdissection 311\n",
      "microenvironment 513\n",
      "micrograms 193\n",
      "microinjection 300\n",
      "microrna 249\n",
      "microsatellite 1529\n",
      "microscope 1233\n",
      "microscopic 268\n",
      "microscopically 231\n",
      "microscopy 1386\n",
      "microtubule 377\n",
      "mid 215\n",
      "middle 1538\n",
      "midostaurin 261\n",
      "might 12639\n",
      "migrated 257\n",
      "migrating 375\n",
      "migration 2610\n",
      "mild 1243\n",
      "milder 414\n",
      "milk 487\n",
      "million 942\n",
      "millipore 1119\n",
      "mim 1102\n",
      "mimic 712\n",
      "mimicking 499\n",
      "mimics 370\n",
      "min 9436\n",
      "mini 787\n",
      "minigene 575\n",
      "minimal 2029\n",
      "minimally 225\n",
      "minimized 310\n",
      "minimum 1048\n",
      "ministry 191\n",
      "minor 1589\n",
      "minority 754\n",
      "minute 467\n",
      "minutes 3232\n",
      "mip 434\n",
      "mir 1468\n",
      "mirna 816\n",
      "mirnas 466\n",
      "mis 244\n",
      "misfolded 230\n",
      "mislocalization 208\n",
      "mismatch 2265\n",
      "mismatches 266\n",
      "missed 301\n",
      "missense 24541\n",
      "missing 578\n",
      "mit 217\n",
      "mitf 412\n",
      "mitochondria 384\n",
      "mitochondrial 844\n",
      "mitogen 1344\n",
      "mitogenic 571\n",
      "mitosis 538\n",
      "mitotic 1310\n",
      "mix 813\n",
      "mixed 2301\n",
      "mixing 249\n",
      "mixture 1696\n",
      "mixtures 373\n",
      "mk 398\n",
      "mkk 1932\n",
      "mkn 194\n",
      "ml 14821\n",
      "mlh 6431\n",
      "mlk 221\n",
      "mll 4224\n",
      "mln 542\n",
      "mlpa 537\n",
      "mm 19343\n",
      "mmc 469\n",
      "mmol 2539\n",
      "mmp 804\n",
      "mmr 4787\n",
      "mms 270\n",
      "mmset 193\n",
      "mmtv 472\n",
      "mn 457\n",
      "mo 1736\n",
      "mobility 1082\n",
      "mock 1266\n",
      "mode 1662\n",
      "model 16552\n",
      "modeled 710\n",
      "modeling 2316\n",
      "modeller 350\n",
      "modelling 205\n",
      "models 9982\n",
      "moderate 2124\n",
      "moderately 988\n",
      "modes 757\n",
      "modest 1568\n",
      "modestly 238\n",
      "modification 1507\n",
      "modifications 1540\n",
      "modified 2684\n",
      "modifier 216\n",
      "modifiers 205\n",
      "modify 433\n",
      "modifying 354\n",
      "modulate 1044\n",
      "modulated 419\n",
      "modulates 423\n",
      "modulating 485\n",
      "modulation 820\n",
      "modulators 193\n",
      "module 895\n",
      "modules 232\n",
      "mody 251\n",
      "moiety 526\n",
      "mol 3257\n",
      "molar 401\n",
      "molecular 18224\n",
      "molecularly 512\n",
      "molecule 5293\n",
      "molecules 5032\n",
      "molt 200\n",
      "monitor 471\n",
      "monitored 1726\n",
      "monitoring 562\n",
      "mono 415\n",
      "monoallelic 383\n",
      "monoclonal 3080\n",
      "monocytic 194\n",
      "monolayer 352\n",
      "monomer 1614\n",
      "monomeric 391\n",
      "monomers 825\n",
      "mononuclear 424\n",
      "monotherapy 439\n",
      "monoubiquitination 225\n",
      "monteiro 270\n",
      "month 1143\n",
      "months 9915\n",
      "morbidity 221\n",
      "moreover 6315\n",
      "morphogenesis 262\n",
      "morphologic 517\n",
      "morphological 808\n",
      "morphologically 307\n",
      "morphology 1723\n",
      "morris 293\n",
      "mortality 694\n",
      "mosaic 246\n",
      "mosaicism 238\n",
      "moss 313\n",
      "mostly 1181\n",
      "mother 705\n",
      "motif 8137\n",
      "motifs 2838\n",
      "motility 1260\n",
      "motion 271\n",
      "motions 622\n",
      "mountain 200\n",
      "mounted 365\n",
      "mounting 235\n",
      "mouse 13389\n",
      "move 280\n",
      "movement 578\n",
      "mp 299\n",
      "mpd 373\n",
      "mpes 296\n",
      "mpl 718\n",
      "mpm 543\n",
      "mpms 265\n",
      "mpn 624\n",
      "mpns 308\n",
      "mpnst 218\n",
      "mr 407\n",
      "mrc 236\n",
      "mre 502\n",
      "mri 386\n",
      "mrna 9280\n",
      "mrnas 628\n",
      "ms 1517\n",
      "msa 390\n",
      "mscv 565\n",
      "msh 7642\n",
      "msi 3468\n",
      "mskcc 244\n",
      "mss 592\n",
      "mst 241\n",
      "mt 1155\n",
      "mta 223\n",
      "mtc 645\n",
      "mtor 10413\n",
      "mtorc 3533\n",
      "mts 535\n",
      "mtt 479\n",
      "mu 244\n",
      "muc 195\n",
      "much 3677\n",
      "mucinous 527\n",
      "mucosa 419\n",
      "mucosal 942\n",
      "mug 262\n",
      "multi 1065\n",
      "multifactorial 1021\n",
      "multiforme 362\n",
      "multiple 13948\n",
      "multiplex 627\n",
      "multiplied 227\n",
      "multitude 208\n",
      "multivariate 1106\n",
      "mum 309\n",
      "murine 3530\n",
      "mus 486\n",
      "muscle 1046\n",
      "musculus 481\n",
      "must 1773\n",
      "mut 1840\n",
      "mutability 506\n",
      "mutagenesis 6410\n",
      "mutagenic 417\n",
      "mutagenized 302\n",
      "mutant 73340\n",
      "mutants 53190\n",
      "mutated 19490\n",
      "mutating 211\n",
      "mutation 105686\n",
      "mutational 7593\n",
      "mutationally 241\n",
      "mutations 238539\n",
      "mutator 337\n",
      "mutl 2172\n",
      "mutpred 230\n",
      "muts 1555\n",
      "mutual 270\n",
      "mutually 1665\n",
      "mv 441\n",
      "mvs 320\n",
      "mx 429\n",
      "mxsf 266\n",
      "myb 218\n",
      "myc 14734\n",
      "mycn 1983\n",
      "myd 362\n",
      "myelodysplasia 208\n",
      "myelodysplastic 633\n",
      "myelofibrosis 374\n",
      "myelogenous 775\n",
      "myeloid 5362\n",
      "myeloma 874\n",
      "myelomonocytic 604\n",
      "myeloproliferative 1299\n",
      "myh 237\n",
      "myod 511\n",
      "myofibroblastic 194\n",
      "myr 589\n",
      "myriad 1185\n",
      "myxoid 327\n",
      "na 4386\n",
      "nacl 2707\n",
      "nadph 639\n",
      "naf 522\n",
      "nah 243\n",
      "naive 360\n",
      "nalm 233\n",
      "name 388\n",
      "named 595\n",
      "namely 887\n",
      "nanomolar 309\n",
      "narrow 310\n",
      "nat 232\n",
      "national 1746\n",
      "native 2959\n",
      "natural 844\n",
      "naturally 885\n",
      "nature 4767\n",
      "nb 709\n",
      "nbs 425\n",
      "nc 475\n",
      "ncbi 763\n",
      "nci 1718\n",
      "nct 802\n",
      "nd 2590\n",
      "ne 1036\n",
      "near 2858\n",
      "nearby 290\n",
      "nearly 2147\n",
      "nec 323\n",
      "necessarily 621\n",
      "necessary 3916\n",
      "neck 1536\n",
      "necrosis 429\n",
      "ned 658\n",
      "neecs 385\n",
      "need 2946\n",
      "needed 2369\n",
      "needle 513\n",
      "needs 516\n",
      "neg 362\n",
      "negative 15334\n",
      "negatively 1678\n",
      "negligible 229\n",
      "neh 762\n",
      "neighborhood 861\n",
      "neighboring 583\n",
      "neither 1903\n",
      "nejm 190\n",
      "nellist 221\n",
      "neo 1042\n",
      "neoadjuvant 343\n",
      "neomorphic 293\n",
      "neomycin 578\n",
      "neonatal 222\n",
      "neoplasia 717\n",
      "neoplasm 532\n",
      "neoplasms 2024\n",
      "neoplastic 1403\n",
      "neor 243\n",
      "ner 479\n",
      "neratinib 1035\n",
      "nerve 375\n",
      "nervous 699\n",
      "nes 408\n",
      "nested 441\n",
      "net 358\n",
      "netherlands 776\n",
      "network 2256\n",
      "networks 687\n",
      "neu 724\n",
      "neural 867\n",
      "neurite 438\n",
      "neuroblastoma 4534\n",
      "neuroblastomas 503\n",
      "neuroendocrine 405\n",
      "neurofibromatosis 545\n",
      "neurofibromin 1984\n",
      "neurological 239\n",
      "neuronal 535\n",
      "neurons 828\n",
      "neutral 12278\n",
      "neutrality 539\n",
      "neutralizing 228\n",
      "neutrophils 320\n",
      "never 3261\n",
      "nevertheless 2051\n",
      "nevi 445\n",
      "new 13653\n",
      "newly 1443\n",
      "next 7965\n",
      "nf 11173\n",
      "nfe 594\n",
      "nfkb 317\n",
      "nfl 531\n",
      "ng 6454\n",
      "ngf 580\n",
      "ngs 549\n",
      "nh 1434\n",
      "nhej 325\n",
      "nherr 260\n",
      "nhgri 466\n",
      "nhl 313\n",
      "nhlbi 207\n",
      "nhuc 315\n",
      "ni 870\n",
      "nic 1219\n",
      "nicd 490\n",
      "nick 560\n",
      "nih 11971\n",
      "nikon 255\n",
      "nilotinib 1470\n",
      "nine 3582\n",
      "nish 255\n",
      "nitrocellulose 862\n",
      "nitrogen 1003\n",
      "nj 695\n",
      "nk 697\n",
      "nkx 932\n",
      "nl 457\n",
      "nlm 213\n",
      "nls 851\n",
      "nm 10063\n",
      "nmc 265\n",
      "nmd 582\n",
      "nmol 3285\n",
      "nmr 915\n",
      "node 1322\n",
      "nodes 597\n",
      "nodule 242\n",
      "nodules 709\n",
      "nog 293\n",
      "noise 195\n",
      "nomenclature 457\n",
      "non 16171\n",
      "nonadenocarcinoma 236\n",
      "noncanonical 298\n",
      "noncoding 234\n",
      "nonconservative 315\n",
      "nonconserved 223\n",
      "nondeleterious 252\n",
      "nondenaturing 267\n",
      "none 5506\n",
      "nonetheless 686\n",
      "nonfunctional 453\n",
      "nonidet 387\n",
      "nonpathogenic 594\n",
      "nonresistant 199\n",
      "nonresponders 362\n",
      "nonsense 3592\n",
      "nonsmokers 211\n",
      "nonspecific 714\n",
      "nonsynonymous 1320\n",
      "noonan 1510\n",
      "normal 19442\n",
      "normalization 626\n",
      "normalized 4138\n",
      "normally 1725\n",
      "north 383\n",
      "northern 522\n",
      "norvegicus 191\n",
      "nos 317\n",
      "notable 787\n",
      "notably 3946\n",
      "notch 19333\n",
      "note 5018\n",
      "noted 3735\n",
      "noteworthy 513\n",
      "noti 261\n",
      "noticed 217\n",
      "noting 218\n",
      "notion 1846\n",
      "novartis 748\n",
      "novel 9852\n",
      "november 354\n",
      "novo 2098\n",
      "nox 193\n",
      "np 1384\n",
      "npg 636\n",
      "npm 2010\n",
      "nr 1302\n",
      "nras 4822\n",
      "nrasg 267\n",
      "nrasq 239\n",
      "nrf 9091\n",
      "nrg 467\n",
      "ns 3566\n",
      "nsclc 11311\n",
      "nsclcs 1311\n",
      "nsd 775\n",
      "nsh 749\n",
      "nt 1720\n",
      "nta 242\n",
      "ntd 532\n",
      "nter 206\n",
      "ntg 542\n",
      "ntm 486\n",
      "ntrk 2847\n",
      "nu 492\n",
      "nuclear 11652\n",
      "nuclease 349\n",
      "nuclei 1609\n",
      "nucleic 541\n",
      "nucleocytoplasmic 226\n",
      "nucleosome 231\n",
      "nucleotide 8886\n",
      "nucleotides 1829\n",
      "nucleus 4070\n",
      "nude 1507\n",
      "null 4969\n",
      "numb 379\n",
      "number 27932\n",
      "numbered 326\n",
      "numbering 1045\n",
      "numbers 4180\n",
      "numerous 1909\n",
      "nup 1358\n",
      "nupage 247\n",
      "nut 725\n",
      "nutrient 609\n",
      "nvp 473\n",
      "ny 985\n",
      "object 259\n",
      "objective 1481\n",
      "observation 4914\n",
      "observations 5398\n",
      "observe 1109\n",
      "observed 32898\n",
      "obtain 2132\n",
      "obtained 14086\n",
      "obtaining 203\n",
      "obvious 936\n",
      "obviously 839\n",
      "oc 496\n",
      "occasional 218\n",
      "occasionally 289\n",
      "occupancy 282\n",
      "occupied 388\n",
      "occupies 292\n",
      "occur 7730\n",
      "occurred 3739\n",
      "occurrence 2859\n",
      "occurrences 307\n",
      "occurring 2857\n",
      "occurs 3945\n",
      "oci 318\n",
      "oct 485\n",
      "october 1185\n",
      "od 472\n",
      "odds 4537\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odyssey 252\n",
      "of 6538\n",
      "off 650\n",
      "offer 756\n",
      "offered 345\n",
      "offers 244\n",
      "offspring 213\n",
      "often 4171\n",
      "og 260\n",
      "oh 381\n",
      "oht 832\n",
      "oil 198\n",
      "olaparib 785\n",
      "old 2539\n",
      "older 685\n",
      "oligo 439\n",
      "oligodendroglioma 286\n",
      "oligodendrogliomas 411\n",
      "oligomer 213\n",
      "oligomeric 396\n",
      "oligomerization 1446\n",
      "oligomers 303\n",
      "oligonucleotide 1251\n",
      "oligonucleotides 1116\n",
      "olympus 316\n",
      "omim 414\n",
      "on 472\n",
      "oncogene 5637\n",
      "oncogenes 2916\n",
      "oncogenesis 1118\n",
      "oncogenic 17098\n",
      "oncogenicity 403\n",
      "oncol 375\n",
      "oncology 1947\n",
      "oncoprotein 547\n",
      "oncoproteins 587\n",
      "one 40627\n",
      "ones 516\n",
      "ongoing 1210\n",
      "online 3164\n",
      "onlinefirst 1252\n",
      "only 412\n",
      "onset 2319\n",
      "onto 2435\n",
      "op 513\n",
      "open 4259\n",
      "operate 218\n",
      "operator 224\n",
      "opportunities 444\n",
      "opportunity 507\n",
      "opposed 322\n",
      "opposing 229\n",
      "opposite 939\n",
      "optical 192\n",
      "optimal 1221\n",
      "optimization 190\n",
      "optimized 440\n",
      "option 503\n",
      "options 3318\n",
      "oral 1325\n",
      "orally 576\n",
      "orange 926\n",
      "order 3445\n",
      "ordered 459\n",
      "oregon 212\n",
      "orf 881\n",
      "orfs 227\n",
      "org 6672\n",
      "organ 781\n",
      "organism 210\n",
      "organisms 251\n",
      "organization 898\n",
      "organs 766\n",
      "orientation 1190\n",
      "orientations 251\n",
      "origin 2380\n",
      "original 2222\n",
      "originally 1280\n",
      "originate 307\n",
      "originated 214\n",
      "originating 404\n",
      "origins 379\n",
      "ors 300\n",
      "orthologous 202\n",
      "orthologs 740\n",
      "orthovanadate 298\n",
      "os 3493\n",
      "osaka 279\n",
      "osteosarcoma 545\n",
      "others 3884\n",
      "otherwise 1171\n",
      "out 531\n",
      "outcome 3519\n",
      "outcomes 1973\n",
      "outer 291\n",
      "outgrowth 673\n",
      "outlier 311\n",
      "outline 261\n",
      "outlined 419\n",
      "output 685\n",
      "outside 2432\n",
      "ov 496\n",
      "ovarian 9628\n",
      "ovary 605\n",
      "ovca 719\n",
      "ovcar 440\n",
      "over 1288\n",
      "overall 11974\n",
      "overcome 1570\n",
      "overexpress 415\n",
      "overexpressed 2870\n",
      "overexpressing 1617\n",
      "overexpression 7606\n",
      "overgrowth 215\n",
      "overlap 1320\n",
      "overlapped 238\n",
      "overlapping 1253\n",
      "overlaps 271\n",
      "overnight 2243\n",
      "overview 701\n",
      "owing 943\n",
      "ox 467\n",
      "oxidation 480\n",
      "oxidative 774\n",
      "oxygen 1301\n",
      "pa 4570\n",
      "pab 560\n",
      "pack 329\n",
      "package 865\n",
      "packaging 445\n",
      "packed 238\n",
      "packing 916\n",
      "packs 224\n",
      "paclitaxel 532\n",
      "pad 288\n",
      "paediatric 244\n",
      "page 6899\n",
      "pai 553\n",
      "pain 236\n",
      "pair 2196\n",
      "paired 2163\n",
      "pairs 2060\n",
      "pairwise 280\n",
      "pak 808\n",
      "pakt 961\n",
      "palb 1584\n",
      "pan 1357\n",
      "pancreas 973\n",
      "pancreatic 3261\n",
      "panel 8831\n",
      "panels 1989\n",
      "panin 528\n",
      "panins 264\n",
      "panitumumab 257\n",
      "paper 644\n",
      "papillary 1475\n",
      "paracrine 262\n",
      "paradigm 554\n",
      "paradoxical 401\n",
      "paraffin 2537\n",
      "paraformaldehyde 310\n",
      "parallel 2471\n",
      "paralogous 332\n",
      "parameter 536\n",
      "parameters 1741\n",
      "parathyroid 278\n",
      "parent 421\n",
      "parental 3226\n",
      "parentheses 250\n",
      "parents 740\n",
      "park 341\n",
      "parp 1828\n",
      "parry 242\n",
      "part 6541\n",
      "partial 5305\n",
      "partially 3035\n",
      "participants 555\n",
      "participate 795\n",
      "participates 467\n",
      "participating 390\n",
      "participation 230\n",
      "particles 262\n",
      "particular 5209\n",
      "particularly 3468\n",
      "partly 642\n",
      "partner 2096\n",
      "partners 1875\n",
      "parts 615\n",
      "pas 256\n",
      "pass 198\n",
      "passage 747\n",
      "passages 221\n",
      "passed 197\n",
      "passenger 1222\n",
      "past 871\n",
      "patch 721\n",
      "patches 219\n",
      "paternal 280\n",
      "path 239\n",
      "pathogenesis 2742\n",
      "pathogenetic 240\n",
      "pathogenic 10554\n",
      "pathogenicity 3241\n",
      "pathologic 655\n",
      "pathological 1559\n",
      "pathologist 395\n",
      "pathologists 350\n",
      "pathology 1979\n",
      "pathophysiology 235\n",
      "pathway 26840\n",
      "pathways 13296\n",
      "patient 24798\n",
      "patients 86405\n",
      "pattern 5000\n",
      "patterns 3243\n",
      "paucity 348\n",
      "pax 910\n",
      "payment 220\n",
      "pb 650\n",
      "pbabe 401\n",
      "pbaf 237\n",
      "pbf 440\n",
      "pbrm 460\n",
      "pbs 3423\n",
      "pbst 270\n",
      "pc 3547\n",
      "pca 913\n",
      "pcaf 204\n",
      "pcc 292\n",
      "pcdna 2831\n",
      "pci 346\n",
      "pcm 250\n",
      "pcmv 760\n",
      "pcna 382\n",
      "pcr 22830\n",
      "pcrs 223\n",
      "pd 4178\n",
      "pdac 515\n",
      "pdb 2405\n",
      "pde 429\n",
      "pdgf 2783\n",
      "pdgfb 285\n",
      "pdgfr 4421\n",
      "pdgfra 12233\n",
      "pdgfrb 1728\n",
      "pdk 551\n",
      "pdx 1977\n",
      "pe 947\n",
      "pea 246\n",
      "peak 1286\n",
      "peaks 755\n",
      "pearson 350\n",
      "pebp 299\n",
      "pediatric 1707\n",
      "pedigree 1910\n",
      "pedigrees 1892\n",
      "peg 198\n",
      "pegfp 334\n",
      "pegfr 248\n",
      "pellet 499\n",
      "pelleted 286\n",
      "pellets 547\n",
      "penetrance 2037\n",
      "penetrant 268\n",
      "penicillin 1006\n",
      "people 294\n",
      "peptide 7215\n",
      "peptidebinding 212\n",
      "peptides 2641\n",
      "per 9359\n",
      "perbb 237\n",
      "percent 1467\n",
      "percentage 4144\n",
      "percentages 524\n",
      "percentile 395\n",
      "perfect 212\n",
      "perform 781\n",
      "performance 1418\n",
      "performed 24144\n",
      "performing 591\n",
      "perhaps 2085\n",
      "perinuclear 218\n",
      "period 1412\n",
      "periods 315\n",
      "peripheral 2644\n",
      "peritoneal 298\n",
      "peritoneum 262\n",
      "perk 1169\n",
      "perkin 450\n",
      "perkinelmer 208\n",
      "permeabilized 358\n",
      "permissive 353\n",
      "permit 290\n",
      "permits 197\n",
      "peroxidase 1166\n",
      "persistent 534\n",
      "person 197\n",
      "personal 2240\n",
      "personalized 529\n",
      "perspective 368\n",
      "pertaining 199\n",
      "perturb 484\n",
      "perturbation 462\n",
      "perturbations 554\n",
      "perturbed 280\n",
      "pest 1252\n",
      "pet 676\n",
      "peters 370\n",
      "pf 2987\n",
      "pfizer 657\n",
      "pfs 2902\n",
      "pfu 200\n",
      "pg 659\n",
      "pgbt 249\n",
      "pgex 310\n",
      "pgk 553\n",
      "pgl 507\n",
      "ph 7974\n",
      "pha 298\n",
      "pharmaceutical 212\n",
      "pharmaceuticals 365\n",
      "pharmacia 630\n",
      "pharmacokinetic 349\n",
      "pharmacologic 522\n",
      "pharmacological 903\n",
      "pharmingen 326\n",
      "phase 8824\n",
      "phases 362\n",
      "phd 712\n",
      "phe 3184\n",
      "phenol 386\n",
      "phenomenon 679\n",
      "phenotype 10209\n",
      "phenotypes 3383\n",
      "phenotypic 1747\n",
      "phenotypically 283\n",
      "phenyl 283\n",
      "phenylalanine 1070\n",
      "phenylindole 194\n",
      "phenylmethylsulfonyl 424\n",
      "pheochromocytoma 288\n",
      "philadelphia 329\n",
      "phone 452\n",
      "phosphatase 9261\n",
      "phosphatases 962\n",
      "phosphate 4037\n",
      "phosphatidylinositol 1887\n",
      "phosphatydilinositol 198\n",
      "phospho 6766\n",
      "phosphoamino 193\n",
      "phosphoinositide 1600\n",
      "phosphoinositides 279\n",
      "phospholipase 363\n",
      "phospholipid 388\n",
      "phosphopeptide 2823\n",
      "phosphoprotein 293\n",
      "phosphorylate 1292\n",
      "phosphorylated 11473\n",
      "phosphorylates 837\n",
      "phosphorylating 299\n",
      "phosphorylation 30094\n",
      "phosphoserine 482\n",
      "phosphospecific 317\n",
      "phosphotyrosine 1581\n",
      "photographed 422\n",
      "photographs 265\n",
      "php 310\n",
      "phts 3222\n",
      "phylogenetic 384\n",
      "physical 984\n",
      "physically 449\n",
      "physicians 452\n",
      "physiologic 644\n",
      "physiological 1785\n",
      "physiologically 239\n",
      "physiology 216\n",
      "pi 16324\n",
      "picked 200\n",
      "picture 640\n",
      "pictures 219\n",
      "pierce 647\n",
      "pig 328\n",
      "pik 14649\n",
      "pilocytic 346\n",
      "pilot 468\n",
      "pim 1637\n",
      "pin 674\n",
      "pink 430\n",
      "pip 3608\n",
      "pipeline 477\n",
      "pipkii 2934\n",
      "piscataway 219\n",
      "pituitary 283\n",
      "pivotal 442\n",
      "piwi 301\n",
      "pjs 650\n",
      "pk 603\n",
      "pka 659\n",
      "pkb 1050\n",
      "pkc 2538\n",
      "pkn 444\n",
      "pl 696\n",
      "pla 214\n",
      "place 709\n",
      "placebo 367\n",
      "placed 769\n",
      "places 220\n",
      "placid 200\n",
      "plasma 4197\n",
      "plasmid 7119\n",
      "plasmids 3737\n",
      "plasticity 249\n",
      "plate 1671\n",
      "plated 2194\n",
      "platelet 1389\n",
      "platelets 258\n",
      "plates 4143\n",
      "platform 1188\n",
      "platforms 230\n",
      "plating 359\n",
      "platinum 1601\n",
      "plausible 458\n",
      "play 4057\n",
      "played 265\n",
      "plays 3260\n",
      "plc 1317\n",
      "pld 355\n",
      "please 1797\n",
      "pleckstrin 200\n",
      "pleural 978\n",
      "plex 232\n",
      "plko 210\n",
      "plot 774\n",
      "plots 739\n",
      "plotted 1379\n",
      "plus 3055\n",
      "plx 1198\n",
      "pm 732\n",
      "pma 222\n",
      "pmc 1402\n",
      "pmek 380\n",
      "pml 332\n",
      "pmol 545\n",
      "pms 4225\n",
      "pmsa 424\n",
      "pmsf 308\n",
      "pnas 371\n",
      "po 558\n",
      "pocket 4953\n",
      "point 11824\n",
      "pointed 202\n",
      "pointing 336\n",
      "points 2301\n",
      "pol 248\n",
      "polar 948\n",
      "polarity 675\n",
      "pold 296\n",
      "pole 1568\n",
      "poly 802\n",
      "polyacrylamide 1318\n",
      "polybrene 391\n",
      "polyclonal 2281\n",
      "polycomb 260\n",
      "polycythemia 478\n",
      "polymerase 3561\n",
      "polymorphic 607\n",
      "polymorphism 3091\n",
      "polymorphisms 2706\n",
      "polypeptide 596\n",
      "polypeptides 564\n",
      "polyphen 1134\n",
      "polyposis 661\n",
      "polyps 638\n",
      "polysomy 261\n",
      "polyubiquitination 220\n",
      "polyvinylidene 283\n",
      "pombe 383\n",
      "ponatinib 2762\n",
      "pone 191\n",
      "pool 1314\n",
      "pooled 1270\n",
      "pools 850\n",
      "poor 3319\n",
      "poorer 388\n",
      "poorly 1673\n",
      "population 7463\n",
      "populations 2495\n",
      "popup 1131\n",
      "portal 275\n",
      "portion 1930\n",
      "portions 376\n",
      "portland 220\n",
      "pose 189\n",
      "position 10667\n",
      "positioned 622\n",
      "positioning 355\n",
      "positions 4996\n",
      "positive 17477\n",
      "positively 1287\n",
      "positives 329\n",
      "positivity 502\n",
      "possess 1114\n",
      "possessed 513\n",
      "possesses 522\n",
      "possibilities 601\n",
      "possibility 5560\n",
      "possible 9424\n",
      "possibly 2651\n",
      "post 2914\n",
      "posterior 1321\n",
      "postnatal 319\n",
      "posttransfection 378\n",
      "posttranslational 552\n",
      "postulate 205\n",
      "postulated 515\n",
      "pot 238\n",
      "potency 2176\n",
      "potent 4070\n",
      "potential 12996\n",
      "potentially 3453\n",
      "potentiate 212\n",
      "potently 1214\n",
      "power 1029\n",
      "powerful 792\n",
      "powerpoint 5656\n",
      "pp 3862\n",
      "ppar 763\n",
      "pparg 421\n",
      "pphln 240\n",
      "ppm 273\n",
      "ppp 1091\n",
      "ppt 287\n",
      "pqe 205\n",
      "pr 5035\n",
      "practice 969\n",
      "pras 270\n",
      "prb 2086\n",
      "prc 801\n",
      "prdm 544\n",
      "pre 3181\n",
      "preceding 344\n",
      "precipitated 1290\n",
      "precipitates 349\n",
      "precipitation 490\n",
      "precise 1174\n",
      "precisely 396\n",
      "precision 303\n",
      "preclinical 2148\n",
      "preclude 195\n",
      "precluded 205\n",
      "precursor 842\n",
      "precursors 400\n",
      "predicated 195\n",
      "predict 3918\n",
      "predicted 11263\n",
      "predicting 1260\n",
      "prediction 3796\n",
      "predictions 2296\n",
      "predictive 2988\n",
      "predictor 962\n",
      "predictors 522\n",
      "predicts 684\n",
      "predispose 956\n",
      "predisposed 238\n",
      "predisposing 536\n",
      "predisposition 1902\n",
      "predominance 253\n",
      "predominant 868\n",
      "predominantly 2035\n",
      "predominate 256\n",
      "preexisting 283\n",
      "preference 477\n",
      "preferential 589\n",
      "preferentially 1382\n",
      "preferred 292\n",
      "preliminary 865\n",
      "premature 1206\n",
      "prep 207\n",
      "preparation 1349\n",
      "preparations 444\n",
      "prepared 3860\n",
      "preponderance 216\n",
      "prerequisite 240\n",
      "presence 20082\n",
      "present 15494\n",
      "presentation 826\n",
      "presented 4160\n",
      "presenting 530\n",
      "presently 295\n",
      "presents 510\n",
      "preserved 302\n",
      "preserving 231\n",
      "press 215\n",
      "pressure 835\n",
      "presumably 1758\n",
      "presumed 384\n",
      "presumptive 374\n",
      "presymptomatic 206\n",
      "pretreated 442\n",
      "pretreatment 891\n",
      "prevalence 2652\n",
      "prevalent 1222\n",
      "prevent 1843\n",
      "prevented 661\n",
      "preventing 884\n",
      "prevention 382\n",
      "prevents 842\n",
      "previous 11270\n",
      "previously 28603\n",
      "primarily 2493\n",
      "primary 18649\n",
      "prime 263\n",
      "primed 233\n",
      "primer 5174\n",
      "primers 6749\n",
      "priming 257\n",
      "primitive 258\n",
      "principal 436\n",
      "principle 721\n",
      "principles 306\n",
      "prior 5764\n",
      "prioritize 191\n",
      "prism 1230\n",
      "private 421\n",
      "prk 304\n",
      "prl 228\n",
      "pro 5553\n",
      "proapoptotic 351\n",
      "probabilities 1868\n",
      "probability 3435\n",
      "probable 467\n",
      "probably 3833\n",
      "proband 1413\n",
      "probands 1457\n",
      "probe 4058\n",
      "probed 1362\n",
      "probes 2439\n",
      "problem 1068\n",
      "problematic 428\n",
      "problems 573\n",
      "procedure 1040\n",
      "procedures 2643\n",
      "process 4224\n",
      "processed 1341\n",
      "processes 2657\n",
      "processing 1464\n",
      "produce 2037\n",
      "produced 3358\n",
      "produces 513\n",
      "producing 727\n",
      "product 4104\n",
      "production 2550\n",
      "productive 217\n",
      "products 5422\n",
      "profi 321\n",
      "proficient 333\n",
      "profile 2343\n",
      "profiled 361\n",
      "profiles 2484\n",
      "profiling 2142\n",
      "profound 701\n",
      "progenitor 1444\n",
      "progenitors 1345\n",
      "progesterone 435\n",
      "prognosis 2883\n",
      "prognostic 2581\n",
      "program 2497\n",
      "programmed 275\n",
      "programs 973\n",
      "progress 1057\n",
      "progressed 707\n",
      "progressing 298\n",
      "progression 13118\n",
      "progressive 1960\n",
      "progressively 198\n",
      "project 1048\n",
      "projects 297\n",
      "proliferate 757\n",
      "proliferated 202\n",
      "proliferating 517\n",
      "proliferation 18911\n",
      "proliferative 1864\n",
      "proline 1743\n",
      "prolonged 1320\n",
      "prolyl 262\n",
      "promega 2376\n",
      "prominent 983\n",
      "promise 461\n",
      "promising 1158\n",
      "promote 5700\n",
      "promoted 1200\n",
      "promoter 10717\n",
      "promoters 1556\n",
      "promotes 2833\n",
      "promoting 2307\n",
      "promotion 470\n",
      "prompted 667\n",
      "prone 1117\n",
      "pronounced 783\n",
      "proof 588\n",
      "propagated 196\n",
      "propeller 258\n",
      "propensity 735\n",
      "proper 1026\n",
      "properly 258\n",
      "properties 5025\n",
      "property 706\n",
      "prophylactic 210\n",
      "propidium 451\n",
      "proportion 4090\n",
      "proportional 502\n",
      "proportions 567\n",
      "propose 1534\n",
      "proposed 3912\n",
      "prospective 896\n",
      "prospectively 389\n",
      "prostate 6793\n",
      "prostatic 198\n",
      "protease 1789\n",
      "proteases 218\n",
      "proteasomal 985\n",
      "proteasome 1442\n",
      "protect 316\n",
      "protected 302\n",
      "protection 469\n",
      "protective 369\n",
      "protects 324\n",
      "protein 84565\n",
      "proteinase 341\n",
      "proteins 40715\n",
      "proteolysis 2131\n",
      "proteolytic 868\n",
      "proteomic 249\n",
      "proto 1101\n",
      "protocol 3216\n",
      "protocols 1366\n",
      "proton 262\n",
      "protooncogene 195\n",
      "prove 722\n",
      "proved 812\n",
      "proven 884\n",
      "provide 10338\n",
      "provided 5486\n",
      "provides 3762\n",
      "providing 1796\n",
      "proximal 1065\n",
      "proximity 1043\n",
      "prs 218\n",
      "ps 1677\n",
      "psa 608\n",
      "pser 1396\n",
      "pseudo 264\n",
      "pseudokinase 519\n",
      "psg 320\n",
      "psi 224\n",
      "pstat 372\n",
      "pt 1782\n",
      "ptau 240\n",
      "ptc 1114\n",
      "ptch 1041\n",
      "ptdins 1313\n",
      "pten 32602\n",
      "ptens 421\n",
      "ptk 401\n",
      "ptp 2842\n",
      "ptpn 1810\n",
      "ptprd 1106\n",
      "ptprt 676\n",
      "ptps 615\n",
      "ptyr 357\n",
      "pu 259\n",
      "public 650\n",
      "publication 703\n",
      "publications 813\n",
      "publicly 373\n",
      "published 6621\n",
      "pubmed 711\n",
      "puc 882\n",
      "pull 1166\n",
      "pulldown 281\n",
      "pulmonary 788\n",
      "pulse 326\n",
      "puma 396\n",
      "punctate 268\n",
      "purchased 2502\n",
      "pure 356\n",
      "purification 1522\n",
      "purified 6229\n",
      "purity 423\n",
      "puro 833\n",
      "puromycin 1365\n",
      "purple 479\n",
      "purpose 989\n",
      "purposes 586\n",
      "putative 3831\n",
      "pv 363\n",
      "pvdf 414\n",
      "pvhl 5021\n",
      "pvhls 193\n",
      "py 1979\n",
      "pymol 767\n",
      "pyrimidine 191\n",
      "pyrophosphate 203\n",
      "qiaamp 338\n",
      "qiagen 2794\n",
      "qpcr 1116\n",
      "qrt 558\n",
      "qualitative 1089\n",
      "qualitatively 199\n",
      "quality 1872\n",
      "quantification 1107\n",
      "quantified 1925\n",
      "quantify 435\n",
      "quantitated 416\n",
      "quantitation 355\n",
      "quantitative 3583\n",
      "quantitatively 467\n",
      "quantity 300\n",
      "question 1478\n",
      "questions 769\n",
      "quick 405\n",
      "quickchange 304\n",
      "quickly 213\n",
      "quiescent 444\n",
      "quikchange 772\n",
      "quite 995\n",
      "quizartinib 309\n",
      "ra 785\n",
      "rab 418\n",
      "rabbit 3005\n",
      "rac 3718\n",
      "race 603\n",
      "rad 5763\n",
      "radiation 1961\n",
      "radioactivity 312\n",
      "radiographic 547\n",
      "radiolabeled 302\n",
      "radiological 234\n",
      "radiotherapy 559\n",
      "raf 31724\n",
      "rafv 293\n",
      "rag 278\n",
      "raise 429\n",
      "raised 854\n",
      "raises 658\n",
      "raising 481\n",
      "ral 337\n",
      "ralgds 347\n",
      "random 2072\n",
      "randomized 594\n",
      "randomly 1060\n",
      "range 7745\n",
      "ranged 1018\n",
      "ranges 240\n",
      "ranging 1305\n",
      "rank 1631\n",
      "ranked 353\n",
      "ransburgh 310\n",
      "rap 335\n",
      "rapamycin 2650\n",
      "rapid 2461\n",
      "rapidly 1610\n",
      "raptor 356\n",
      "rar 311\n",
      "rara 394\n",
      "rare 7781\n",
      "rarely 1141\n",
      "rarity 235\n",
      "rars 332\n",
      "ras 43600\n",
      "rasa 426\n",
      "rasg 838\n",
      "rasgap 723\n",
      "rash 366\n",
      "rasv 463\n",
      "raswt 312\n",
      "rat 1991\n",
      "rate 9780\n",
      "rates 4707\n",
      "rather 4507\n",
      "ratio 6733\n",
      "rational 475\n",
      "rationale 912\n",
      "rationalize 360\n",
      "rationalized 406\n",
      "ratios 2021\n",
      "rats 290\n",
      "rattus 197\n",
      "raw 497\n",
      "ray 935\n",
      "rb 3824\n",
      "rbd 1257\n",
      "rbm 1189\n",
      "rbp 205\n",
      "rbx 702\n",
      "rc 357\n",
      "rcas 283\n",
      "rcc 1754\n",
      "rd 390\n",
      "re 1772\n",
      "reach 885\n",
      "reached 1113\n",
      "reaching 312\n",
      "reacp 468\n",
      "reaction 6156\n",
      "reactions 2371\n",
      "reactivation 590\n",
      "reactive 470\n",
      "reactivity 240\n",
      "read 1265\n",
      "reader 347\n",
      "readily 1416\n",
      "reading 934\n",
      "readout 333\n",
      "reads 2905\n",
      "ready 360\n",
      "reagent 2044\n",
      "reagents 1185\n",
      "real 2022\n",
      "rearranged 1387\n",
      "rearrangement 2404\n",
      "rearrangements 4253\n",
      "reason 1034\n",
      "reasonable 497\n",
      "reasoned 505\n",
      "reasons 956\n",
      "recapitulate 347\n",
      "recapitulated 234\n",
      "receive 553\n",
      "received 3736\n",
      "receiving 1233\n",
      "recent 8411\n",
      "recently 10323\n",
      "receptor 25678\n",
      "receptors 6867\n",
      "recessive 405\n",
      "recipient 441\n",
      "recipients 365\n",
      "reciprocal 583\n",
      "recist 559\n",
      "reclassified 344\n",
      "recognition 4104\n",
      "recognize 767\n",
      "recognized 1830\n",
      "recognizes 945\n",
      "recognizing 360\n",
      "recombinant 3929\n",
      "recombinase 417\n",
      "recombination 3782\n",
      "recombineering 194\n",
      "recommendations 944\n",
      "recommended 1038\n",
      "reconstituted 636\n",
      "reconstitution 518\n",
      "recorded 1206\n",
      "records 442\n",
      "recovered 767\n",
      "recovery 354\n",
      "recruit 665\n",
      "recruited 933\n",
      "recruiting 309\n",
      "recruitment 2775\n",
      "recruits 539\n",
      "rectal 191\n",
      "rectum 267\n",
      "recurrence 1505\n",
      "recurrent 6458\n",
      "recurrently 871\n",
      "recurring 384\n",
      "red 5584\n",
      "redistribution 207\n",
      "reduce 2024\n",
      "reduced 13216\n",
      "reduces 934\n",
      "reducing 844\n",
      "reduction 4907\n",
      "reductions 500\n",
      "redundancy 258\n",
      "redundant 291\n",
      "ref 5601\n",
      "refer 516\n",
      "reference 2936\n",
      "references 1194\n",
      "referred 1499\n",
      "refers 310\n",
      "refined 446\n",
      "refinement 576\n",
      "reflect 1992\n",
      "reflected 705\n",
      "reflecting 585\n",
      "reflections 205\n",
      "reflects 885\n",
      "refractory 1899\n",
      "refs 1873\n",
      "refseq 314\n",
      "reg 322\n",
      "regard 1481\n",
      "regarded 397\n",
      "regarding 1416\n",
      "regardless 1064\n",
      "regeneration 217\n",
      "regimen 809\n",
      "regimens 1514\n",
      "region 22867\n",
      "regional 281\n",
      "regions 10404\n",
      "registry 229\n",
      "regorafenib 527\n",
      "regression 2588\n",
      "regressions 387\n",
      "regular 423\n",
      "regulate 3505\n",
      "regulated 6113\n",
      "regulates 2954\n",
      "regulating 2054\n",
      "regulation 9807\n",
      "regulator 1869\n",
      "regulators 1570\n",
      "regulatory 6938\n",
      "reintroduction 281\n",
      "rela 260\n",
      "relapse 1849\n",
      "relapsed 1482\n",
      "relate 263\n",
      "related 12420\n",
      "relates 231\n",
      "relation 655\n",
      "relationship 2358\n",
      "relationships 664\n",
      "relative 10848\n",
      "relatively 5226\n",
      "relatives 1489\n",
      "release 1483\n",
      "released 598\n",
      "relevance 2838\n",
      "relevant 5116\n",
      "reliable 849\n",
      "relies 568\n",
      "relieve 245\n",
      "rely 586\n",
      "remain 2086\n",
      "remainder 331\n",
      "remained 1946\n",
      "remaining 5469\n",
      "remains 5249\n",
      "remarkable 693\n",
      "remarkably 1307\n",
      "reminiscent 598\n",
      "remission 991\n",
      "remodeling 910\n",
      "removal 1243\n",
      "remove 968\n",
      "removed 1730\n",
      "removes 289\n",
      "removing 498\n",
      "renal 2304\n",
      "render 416\n",
      "rendered 561\n",
      "rendering 304\n",
      "renders 404\n",
      "renewal 596\n",
      "renilla 1236\n",
      "reorganization 214\n",
      "repair 9288\n",
      "repaired 317\n",
      "repeat 4001\n",
      "repeated 1383\n",
      "repeatedly 226\n",
      "repeats 4814\n",
      "repetitive 225\n",
      "replace 417\n",
      "replaced 1597\n",
      "replacement 1085\n",
      "replacements 193\n",
      "replacing 446\n",
      "replicate 486\n",
      "replicates 827\n",
      "replication 1772\n",
      "replicative 290\n",
      "report 6452\n",
      "reported 28632\n",
      "reportedly 213\n",
      "reporter 8016\n",
      "reporters 285\n",
      "reporting 386\n",
      "reports 4538\n",
      "repository 262\n",
      "represent 6220\n",
      "representation 2123\n",
      "representations 214\n",
      "representative 4411\n",
      "represented 2399\n",
      "representing 1634\n",
      "represents 3321\n",
      "repress 426\n",
      "repressed 690\n",
      "represses 322\n",
      "repression 1447\n",
      "repressive 281\n",
      "repressor 740\n",
      "reprints 441\n",
      "reprobed 291\n",
      "reproducibility 194\n",
      "reproducible 489\n",
      "request 555\n",
      "requests 336\n",
      "require 4644\n",
      "required 12408\n",
      "requirement 1283\n",
      "requirements 466\n",
      "requires 2739\n",
      "requiring 327\n",
      "res 3291\n",
      "rescue 2602\n",
      "rescued 918\n",
      "rescues 219\n",
      "research 8535\n",
      "researchers 458\n",
      "resected 899\n",
      "resection 1077\n",
      "resemble 341\n",
      "resembles 378\n",
      "resembling 417\n",
      "resequencing 366\n",
      "reserved 686\n",
      "reside 512\n",
      "resides 324\n",
      "residual 1394\n",
      "residue 14591\n",
      "residues 29669\n",
      "resin 603\n",
      "resistance 29094\n",
      "resistant 15613\n",
      "resolution 2546\n",
      "resolve 254\n",
      "resolved 1575\n",
      "resonance 589\n",
      "resource 603\n",
      "resources 240\n",
      "respect 2289\n",
      "respective 1516\n",
      "respectively 16192\n",
      "respiratory 199\n",
      "respond 2009\n",
      "responded 806\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "responder 328\n",
      "responders 1013\n",
      "responding 417\n",
      "response 27108\n",
      "responses 5809\n",
      "responsible 3721\n",
      "responsive 2715\n",
      "responsiveness 1561\n",
      "rest 772\n",
      "resting 190\n",
      "restoration 583\n",
      "restore 1014\n",
      "restored 857\n",
      "restores 392\n",
      "restoring 257\n",
      "restricted 1522\n",
      "restriction 1813\n",
      "result 13409\n",
      "resultant 493\n",
      "resulted 7102\n",
      "resulting 8422\n",
      "results 45879\n",
      "resuspended 1215\n",
      "ret 6762\n",
      "retain 1159\n",
      "retained 2757\n",
      "retaining 307\n",
      "retains 719\n",
      "retardation 567\n",
      "retention 1006\n",
      "reticulocyte 301\n",
      "retinal 272\n",
      "retinoblastoma 822\n",
      "retinoic 203\n",
      "retrieval 308\n",
      "retrieved 353\n",
      "retrospective 821\n",
      "retrospectively 495\n",
      "retroviral 2039\n",
      "retrovirally 291\n",
      "retrovirus 1174\n",
      "retroviruses 684\n",
      "rev 447\n",
      "reveal 2730\n",
      "revealed 12162\n",
      "revealing 526\n",
      "reveals 1512\n",
      "reversal 230\n",
      "reverse 4954\n",
      "reversed 441\n",
      "reversible 798\n",
      "reversion 193\n",
      "review 3783\n",
      "reviewed 3010\n",
      "reviews 207\n",
      "revised 302\n",
      "rfs 274\n",
      "rg 641\n",
      "rhabdoid 198\n",
      "rhabdomyosarcoma 267\n",
      "rhd 238\n",
      "rheb 2201\n",
      "rho 1209\n",
      "rhoa 3793\n",
      "ri 414\n",
      "ribbon 667\n",
      "ribose 218\n",
      "ribosomal 582\n",
      "rich 2739\n",
      "rictor 654\n",
      "ridge 383\n",
      "right 4515\n",
      "rights 666\n",
      "rigid 748\n",
      "rigidity 231\n",
      "rigorous 194\n",
      "rii 501\n",
      "ring 5269\n",
      "rinsed 287\n",
      "ripa 547\n",
      "rise 1259\n",
      "risk 12416\n",
      "risks 1200\n",
      "rit 413\n",
      "rizos 273\n",
      "rmce 2582\n",
      "rms 215\n",
      "rmsd 628\n",
      "rna 12760\n",
      "rnai 1140\n",
      "rnapii 269\n",
      "rnas 603\n",
      "rnase 1073\n",
      "rneasy 473\n",
      "rnf 587\n",
      "ro 194\n",
      "roberts 232\n",
      "robust 1979\n",
      "robustly 304\n",
      "roc 318\n",
      "roche 2196\n",
      "rock 725\n",
      "role 20711\n",
      "roles 3388\n",
      "room 1630\n",
      "root 375\n",
      "ror 194\n",
      "ros 5549\n",
      "rosa 703\n",
      "rotation 216\n",
      "roughly 515\n",
      "round 738\n",
      "rounds 256\n",
      "routine 1027\n",
      "routinely 612\n",
      "row 993\n",
      "rows 202\n",
      "rp 983\n",
      "rpm 471\n",
      "rpmi 1908\n",
      "rppa 383\n",
      "rps 356\n",
      "rr 1097\n",
      "rras 496\n",
      "rs 1371\n",
      "rsk 495\n",
      "rst 270\n",
      "rt 5783\n",
      "rtk 2080\n",
      "rtks 1292\n",
      "rtnf 240\n",
      "rule 926\n",
      "ruled 209\n",
      "rules 950\n",
      "run 1041\n",
      "running 192\n",
      "runs 300\n",
      "runt 316\n",
      "runx 4545\n",
      "ruxolitinib 1110\n",
      "rv 371\n",
      "rwpe 210\n",
      "sa 1004\n",
      "saccharomyces 440\n",
      "sacrificed 323\n",
      "safety 690\n",
      "sali 214\n",
      "saline 752\n",
      "salivary 193\n",
      "salt 1479\n",
      "sam 296\n",
      "sample 9136\n",
      "samples 26678\n",
      "sampling 359\n",
      "san 1490\n",
      "sandwich 610\n",
      "sanger 2701\n",
      "santa 3658\n",
      "saos 997\n",
      "sapiens 887\n",
      "sara 928\n",
      "sarcoma 1943\n",
      "sarcomas 643\n",
      "sarcomatoid 193\n",
      "sas 230\n",
      "sasaki 255\n",
      "saturation 361\n",
      "sb 1100\n",
      "sbc 696\n",
      "sbcl 196\n",
      "sbcs 266\n",
      "sbe 233\n",
      "sc 1597\n",
      "scaffold 804\n",
      "scaffolding 687\n",
      "scale 2791\n",
      "scaled 198\n",
      "scan 921\n",
      "scanned 321\n",
      "scanner 224\n",
      "scanning 1289\n",
      "scans 387\n",
      "scatter 212\n",
      "scattered 331\n",
      "scc 932\n",
      "sccoht 278\n",
      "sccs 407\n",
      "scd 400\n",
      "scei 894\n",
      "scenario 439\n",
      "scf 1505\n",
      "sch 346\n",
      "schematic 2246\n",
      "scheme 799\n",
      "schemes 293\n",
      "schizosaccharomyces 193\n",
      "scholar 684\n",
      "school 770\n",
      "sci 196\n",
      "scid 436\n",
      "science 1646\n",
      "sciences 372\n",
      "scientific 897\n",
      "scintillation 244\n",
      "sclc 1305\n",
      "sclerosis 468\n",
      "scnas 433\n",
      "sco 1482\n",
      "scope 200\n",
      "score 4077\n",
      "scored 2035\n",
      "scores 2523\n",
      "scoring 528\n",
      "scp 853\n",
      "scrambled 395\n",
      "screen 3919\n",
      "screened 2824\n",
      "screening 4637\n",
      "screens 1344\n",
      "sd 4922\n",
      "sdc 192\n",
      "sdm 496\n",
      "sds 5285\n",
      "sdspage 226\n",
      "se 823\n",
      "search 1444\n",
      "searched 480\n",
      "sec 660\n",
      "second 11718\n",
      "secondary 10756\n",
      "seconds 655\n",
      "secretase 800\n",
      "secreted 342\n",
      "secretion 434\n",
      "secretory 279\n",
      "section 2983\n",
      "sections 2603\n",
      "see 9802\n",
      "seeded 2465\n",
      "seeding 319\n",
      "seem 1420\n",
      "seemed 632\n",
      "seems 2891\n",
      "seen 6817\n",
      "segment 2660\n",
      "segments 736\n",
      "segregated 439\n",
      "segregates 202\n",
      "segregation 2356\n",
      "seizures 204\n",
      "select 957\n",
      "selectable 225\n",
      "selected 7483\n",
      "selecting 346\n",
      "selection 5618\n",
      "selective 4147\n",
      "selectively 1456\n",
      "selectivity 813\n",
      "self 1562\n",
      "selleck 207\n",
      "selumetinib 249\n",
      "sem 867\n",
      "sema 241\n",
      "semi 279\n",
      "senescence 1430\n",
      "sense 988\n",
      "sensitive 11103\n",
      "sensitivities 537\n",
      "sensitivity 15025\n",
      "sensitize 275\n",
      "sensitized 257\n",
      "sensitizing 395\n",
      "sensor 250\n",
      "seoul 198\n",
      "separate 1967\n",
      "separated 2262\n",
      "separately 758\n",
      "separation 538\n",
      "sepharose 909\n",
      "september 682\n",
      "seq 2204\n",
      "sequence 27563\n",
      "sequenced 5545\n",
      "sequencer 541\n",
      "sequences 9763\n",
      "sequencing 22506\n",
      "sequenom 326\n",
      "sequential 750\n",
      "sequentially 241\n",
      "sequestration 251\n",
      "ser 8230\n",
      "serial 1058\n",
      "series 5854\n",
      "serine 3335\n",
      "serious 309\n",
      "serous 1161\n",
      "serum 8897\n",
      "serve 1702\n",
      "served 675\n",
      "server 215\n",
      "serves 917\n",
      "service 191\n",
      "set 11197\n",
      "setd 725\n",
      "sets 2339\n",
      "setting 2126\n",
      "settings 774\n",
      "seven 5442\n",
      "seventy 199\n",
      "several 19729\n",
      "severe 2490\n",
      "severely 840\n",
      "severity 808\n",
      "sex 1322\n",
      "sf 6061\n",
      "sg 547\n",
      "sh 4140\n",
      "shaded 264\n",
      "shape 373\n",
      "shaped 388\n",
      "share 1759\n",
      "shared 1023\n",
      "shares 375\n",
      "sharp 193\n",
      "shc 417\n",
      "shed 372\n",
      "sheet 1048\n",
      "sheets 343\n",
      "shell 224\n",
      "sherr 189\n",
      "shh 420\n",
      "shi 277\n",
      "shibata 200\n",
      "shift 2647\n",
      "shifted 606\n",
      "shifting 248\n",
      "shifts 472\n",
      "shock 384\n",
      "short 3964\n",
      "shortened 243\n",
      "shorter 1260\n",
      "show 14340\n",
      "showed 26680\n",
      "showing 6431\n",
      "shown 43807\n",
      "shows 6011\n",
      "shp 3463\n",
      "shq 281\n",
      "shrinkage 224\n",
      "shrna 3121\n",
      "shrnas 635\n",
      "si 1196\n",
      "siblings 271\n",
      "side 6603\n",
      "sided 699\n",
      "sideroblasts 235\n",
      "sift 1948\n",
      "sigma 3947\n",
      "sign 222\n",
      "signal 10033\n",
      "signaling 35038\n",
      "signalling 4624\n",
      "signals 6084\n",
      "signature 1714\n",
      "signatures 1092\n",
      "signifi 919\n",
      "significance 7409\n",
      "significant 18444\n",
      "significantly 16703\n",
      "signs 718\n",
      "silenced 396\n",
      "silencing 1477\n",
      "silent 1197\n",
      "silico 2602\n",
      "silver 253\n",
      "similar 23770\n",
      "similarities 523\n",
      "similarity 1062\n",
      "similarly 5123\n",
      "simple 1184\n",
      "simplified 266\n",
      "simply 860\n",
      "simulated 288\n",
      "simulation 377\n",
      "simulations 928\n",
      "simultaneous 497\n",
      "simultaneously 1023\n",
      "since 6928\n",
      "singh 201\n",
      "single 15917\n",
      "sip 240\n",
      "sirna 4710\n",
      "sirnas 1181\n",
      "sister 514\n",
      "site 26663\n",
      "sites 13847\n",
      "situ 1972\n",
      "situated 262\n",
      "situation 585\n",
      "six 7921\n",
      "sixteen 268\n",
      "sixty 248\n",
      "size 7294\n",
      "sized 304\n",
      "sizes 691\n",
      "sk 1379\n",
      "skbr 206\n",
      "skeletal 656\n",
      "ski 595\n",
      "skin 3813\n",
      "skipping 1068\n",
      "skmel 200\n",
      "skp 626\n",
      "slc 788\n",
      "slegtenhorst 236\n",
      "slide 2821\n",
      "slides 1409\n",
      "slight 791\n",
      "slightly 1927\n",
      "sloan 914\n",
      "slow 673\n",
      "slower 389\n",
      "slowly 335\n",
      "sm 366\n",
      "smad 32851\n",
      "smads 1552\n",
      "small 17945\n",
      "smaller 1741\n",
      "smarca 921\n",
      "smarcb 217\n",
      "smc 206\n",
      "smith 373\n",
      "smo 5073\n",
      "smoked 273\n",
      "smoker 851\n",
      "smokers 2077\n",
      "smoking 1540\n",
      "smooth 288\n",
      "smurf 485\n",
      "snail 251\n",
      "snap 398\n",
      "snd 279\n",
      "snf 1325\n",
      "snp 2571\n",
      "snps 1612\n",
      "snu 852\n",
      "snv 331\n",
      "snvs 736\n",
      "so 377\n",
      "society 804\n",
      "socs 757\n",
      "sodium 1825\n",
      "soft 2790\n",
      "software 4013\n",
      "sole 448\n",
      "solely 899\n",
      "solid 3457\n",
      "soluble 1148\n",
      "solution 2764\n",
      "solutions 334\n",
      "solved 713\n",
      "solvent 1400\n",
      "somatic 15424\n",
      "somatically 747\n",
      "sometimes 542\n",
      "somewhat 746\n",
      "sonicated 235\n",
      "sonication 255\n",
      "soon 220\n",
      "sorafenib 2515\n",
      "sorted 703\n",
      "sorting 647\n",
      "sos 2010\n",
      "sought 1512\n",
      "source 1648\n",
      "sources 1609\n",
      "sourceturn 512\n",
      "south 381\n",
      "southern 986\n",
      "sox 1698\n",
      "sp 1250\n",
      "spa 212\n",
      "space 721\n",
      "span 327\n",
      "spanish 282\n",
      "spanning 1177\n",
      "spans 207\n",
      "spatial 434\n",
      "spatially 214\n",
      "spearman 417\n",
      "speci 208\n",
      "special 219\n",
      "species 3537\n",
      "specifi 530\n",
      "specific 27878\n",
      "specifically 5280\n",
      "specificities 490\n",
      "specificity 5946\n",
      "specified 513\n",
      "specimen 1209\n",
      "specimens 5970\n",
      "spectra 1254\n",
      "spectral 220\n",
      "spectrometry 969\n",
      "spectroscopy 427\n",
      "spectrum 4045\n",
      "speculate 1039\n",
      "speculated 317\n",
      "spheres 297\n",
      "spheroids 481\n",
      "spin 252\n",
      "spinal 214\n",
      "spindle 989\n",
      "spine 660\n",
      "spite 244\n",
      "spleen 1398\n",
      "splenic 196\n",
      "splenomegaly 291\n",
      "splice 6916\n",
      "spliced 1069\n",
      "spliceosome 385\n",
      "splicing 9218\n",
      "split 923\n",
      "spontaneous 1125\n",
      "spontaneously 374\n",
      "spop 10113\n",
      "spopbtb 266\n",
      "spopmath 380\n",
      "spopwt 271\n",
      "sporadic 4507\n",
      "spot 2050\n",
      "spots 1132\n",
      "spread 691\n",
      "spreading 254\n",
      "spreads 195\n",
      "spry 210\n",
      "spss 493\n",
      "sq 473\n",
      "sqcc 285\n",
      "squamous 3505\n",
      "square 697\n",
      "squared 204\n",
      "squares 279\n",
      "sr 408\n",
      "src 5104\n",
      "srf 415\n",
      "srsf 1053\n",
      "sry 248\n",
      "ss 843\n",
      "ssa 1499\n",
      "ssc 222\n",
      "sscp 899\n",
      "ssr 434\n",
      "st 2065\n",
      "stability 9385\n",
      "stabilization 1712\n",
      "stabilize 1588\n",
      "stabilized 1028\n",
      "stabilizes 694\n",
      "stabilizing 946\n",
      "stable 7010\n",
      "stably 4088\n",
      "stacking 231\n",
      "stag 352\n",
      "stage 5858\n",
      "stages 1596\n",
      "staging 401\n",
      "stain 598\n",
      "stained 4017\n",
      "staining 7546\n",
      "standard 6326\n",
      "standardized 1087\n",
      "standards 352\n",
      "star 270\n",
      "stark 413\n",
      "start 1942\n",
      "started 622\n",
      "starting 1091\n",
      "starvation 733\n",
      "starved 1842\n",
      "stat 10577\n",
      "stata 240\n",
      "state 7480\n",
      "states 2320\n",
      "statistical 4916\n",
      "statistically 2891\n",
      "statistics 822\n",
      "stature 261\n",
      "status 10742\n",
      "ste 233\n",
      "steady 978\n",
      "stem 6292\n",
      "step 3217\n",
      "steps 991\n",
      "stepwise 242\n",
      "stereo 189\n",
      "steric 971\n",
      "sterically 227\n",
      "sterile 234\n",
      "steroid 437\n",
      "sti 733\n",
      "stick 443\n",
      "still 5472\n",
      "stimulate 1866\n",
      "stimulated 4054\n",
      "stimulates 724\n",
      "stimulating 659\n",
      "stimulation 5626\n",
      "stimulatory 417\n",
      "stimuli 608\n",
      "stimulus 204\n",
      "stk 1038\n",
      "stock 313\n",
      "stoichiometry 272\n",
      "stomach 1337\n",
      "stop 2569\n",
      "stopped 465\n",
      "stored 716\n",
      "strad 208\n",
      "straightforward 459\n",
      "strain 1348\n",
      "strains 672\n",
      "strand 4294\n",
      "stranded 1016\n",
      "strands 1040\n",
      "stratagene 1430\n",
      "strategies 1979\n",
      "strategy 2483\n",
      "stratification 574\n",
      "stratified 268\n",
      "strength 459\n",
      "streptavidin 647\n",
      "streptomycin 1009\n",
      "stress 2342\n",
      "stresses 189\n",
      "stretch 334\n",
      "strict 248\n",
      "strictly 467\n",
      "striking 1203\n",
      "strikingly 933\n",
      "stringent 642\n",
      "stripped 537\n",
      "strn 202\n",
      "stroma 563\n",
      "stromal 2106\n",
      "strong 7225\n",
      "stronger 919\n",
      "strongest 381\n",
      "strongly 5261\n",
      "structural 13896\n",
      "structurally 1760\n",
      "structure 24695\n",
      "structures 6666\n",
      "student 1392\n",
      "studied 5186\n",
      "studies 31642\n",
      "study 36975\n",
      "studying 401\n",
      "su 1728\n",
      "sub 711\n",
      "subcellular 1868\n",
      "subclonal 259\n",
      "subclone 208\n",
      "subcloned 1288\n",
      "subclones 478\n",
      "subcloning 220\n",
      "subcutaneous 557\n",
      "subcutaneously 491\n",
      "subdivided 238\n",
      "subdomain 546\n",
      "subdomains 339\n",
      "subfamily 572\n",
      "subg 334\n",
      "subgroup 1470\n",
      "subgroups 1327\n",
      "subject 1318\n",
      "subjected 4263\n",
      "subjects 2024\n",
      "submission 515\n",
      "submitted 850\n",
      "submitters 294\n",
      "suboptimal 455\n",
      "subpopulation 206\n",
      "subpopulations 209\n",
      "subsequent 4175\n",
      "subsequently 3156\n",
      "subset 5826\n",
      "subsets 1027\n",
      "substantial 2078\n",
      "substantially 1626\n",
      "substitute 322\n",
      "substituted 972\n",
      "substituting 350\n",
      "substitution 11604\n",
      "substitutions 10768\n",
      "substrate 9156\n",
      "substrates 3965\n",
      "subtle 1280\n",
      "subtype 2588\n",
      "subtypes 2861\n",
      "subunit 6292\n",
      "subunits 2684\n",
      "success 587\n",
      "successful 905\n",
      "successfully 1208\n",
      "successive 203\n",
      "suffered 223\n",
      "sufficient 4732\n",
      "sufficiently 675\n",
      "sufu 296\n",
      "suggest 13971\n",
      "suggested 6502\n",
      "suggesting 12594\n",
      "suggestion 297\n",
      "suggestive 424\n",
      "suggests 8426\n",
      "suitable 704\n",
      "suite 197\n",
      "sulfate 900\n",
      "sulfoxide 232\n",
      "sum 970\n",
      "summaries 189\n",
      "summarize 200\n",
      "summarized 1594\n",
      "summarizes 315\n",
      "summary 2992\n",
      "sumoylation 227\n",
      "sun 706\n",
      "sunitinib 3038\n",
      "sup 509\n",
      "super 545\n",
      "superbinder 270\n",
      "superfamily 647\n",
      "superimposed 434\n",
      "superimposition 262\n",
      "superior 629\n",
      "supernatant 1201\n",
      "supernatants 653\n",
      "superscript 386\n",
      "supervised 269\n",
      "supm 325\n",
      "supp 1794\n",
      "suppl 299\n",
      "supplement 605\n",
      "supplemental 5084\n",
      "supplementary 29311\n",
      "supplemented 3173\n",
      "supplied 272\n",
      "supply 236\n",
      "support 5445\n",
      "supported 2732\n",
      "supporting 3270\n",
      "supportive 189\n",
      "supports 1378\n",
      "suppress 1949\n",
      "suppressed 1720\n",
      "suppresses 973\n",
      "suppressing 728\n",
      "suppression 4632\n",
      "suppressive 1619\n",
      "suppressor 9485\n",
      "suppressors 1029\n",
      "surface 8164\n",
      "surfaces 551\n",
      "surgery 1729\n",
      "surgical 1382\n",
      "surgically 432\n",
      "surprising 1004\n",
      "surprisingly 1382\n",
      "surrogate 372\n",
      "surrounded 320\n",
      "surrounding 1226\n",
      "surveillance 685\n",
      "survey 314\n",
      "surveyor 557\n",
      "survival 19095\n",
      "survive 307\n",
      "survived 273\n",
      "surviving 248\n",
      "susceptibility 2411\n",
      "susceptible 747\n",
      "suspected 878\n",
      "suspended 335\n",
      "suspension 413\n",
      "sustain 242\n",
      "sustained 1246\n",
      "suv 239\n",
      "sv 812\n",
      "sw 1474\n",
      "sweden 199\n",
      "swi 681\n",
      "swiss 225\n",
      "switch 3177\n",
      "switched 578\n",
      "switching 344\n",
      "switzerland 449\n",
      "sy 337\n",
      "sybr 409\n",
      "symbols 343\n",
      "symmetry 214\n",
      "symptomatic 212\n",
      "symptoms 883\n",
      "syndrome 8921\n",
      "syndromes 1963\n",
      "synergistic 523\n",
      "synergistically 200\n",
      "synergize 221\n",
      "synergy 266\n",
      "syngeneic 220\n",
      "synonymous 1278\n",
      "synthase 410\n",
      "synthesis 2405\n",
      "synthesized 1222\n",
      "synthetic 1249\n",
      "system 10916\n",
      "systematic 1088\n",
      "systematically 490\n",
      "systemic 1227\n",
      "systems 2839\n",
      "ta 632\n",
      "tab 738\n",
      "tabdownload 2951\n",
      "table 52734\n",
      "tables 4168\n",
      "tacc 686\n",
      "tad 608\n",
      "tae 796\n",
      "tag 1861\n",
      "tagged 6022\n",
      "tags 332\n",
      "tail 2047\n",
      "tailed 788\n",
      "taiwan 544\n",
      "taiwanese 197\n",
      "takara 264\n",
      "take 554\n",
      "taken 4784\n",
      "takes 296\n",
      "taking 526\n",
      "tal 356\n",
      "talk 342\n",
      "tamoxifen 372\n",
      "tandem 1934\n",
      "tap 296\n",
      "taq 550\n",
      "taqman 401\n",
      "target 16077\n",
      "targetable 320\n",
      "targeted 8603\n",
      "targeting 8033\n",
      "targets 6989\n",
      "tartaglia 267\n",
      "tat 428\n",
      "tau 864\n",
      "taxane 278\n",
      "taz 403\n",
      "tbr 219\n",
      "tbrii 217\n",
      "tbs 294\n",
      "tbst 279\n",
      "tc 485\n",
      "tca 270\n",
      "tcc 331\n",
      "tcf 2186\n",
      "tcga 3365\n",
      "tcr 410\n",
      "tcs 269\n",
      "tct 535\n",
      "td 1074\n",
      "te 395\n",
      "technical 561\n",
      "technically 206\n",
      "technique 644\n",
      "techniques 952\n",
      "technologies 3353\n",
      "technology 2199\n",
      "tel 1488\n",
      "telangiectasia 192\n",
      "teleconferences 196\n",
      "telomerase 730\n",
      "telomere 420\n",
      "telomeres 249\n",
      "telomeric 264\n",
      "temperature 3313\n",
      "temperatures 449\n",
      "template 1761\n",
      "templates 360\n",
      "temporal 294\n",
      "tempting 288\n",
      "ten 1707\n",
      "tend 595\n",
      "tended 583\n",
      "tendency 449\n",
      "tensin 646\n",
      "tensins 198\n",
      "tentatively 250\n",
      "ter 404\n",
      "term 1614\n",
      "termed 1059\n",
      "terminal 18793\n",
      "terminally 466\n",
      "terminated 275\n",
      "termination 793\n",
      "terminator 642\n",
      "termini 512\n",
      "terminus 5651\n",
      "terms 2365\n",
      "ternary 314\n",
      "tert 2316\n",
      "tertiary 533\n",
      "test 12808\n",
      "tested 14440\n",
      "testing 4551\n",
      "testis 232\n",
      "testosterone 244\n",
      "tests 1906\n",
      "tet 6105\n",
      "tethered 214\n",
      "tetracycline 486\n",
      "tetramer 1066\n",
      "tetrameric 331\n",
      "tetramerization 809\n",
      "tetramers 364\n",
      "tetraodon 200\n",
      "texas 277\n",
      "text 3202\n",
      "tf 403\n",
      "tfap 265\n",
      "tg 734\n",
      "tgf 7311\n",
      "tgfb 211\n",
      "tgfbr 2037\n",
      "tgg 283\n",
      "tgt 270\n",
      "th 1365\n",
      "thd 200\n",
      "theoretical 247\n",
      "theory 294\n",
      "therapeutic 8261\n",
      "therapeutically 329\n",
      "therapeutics 804\n",
      "therapies 3997\n",
      "therapy 12445\n",
      "thereafter 421\n",
      "thereby 2977\n",
      "therefore 16682\n",
      "thermal 562\n",
      "thermo 695\n",
      "thermodynamic 533\n",
      "thick 298\n",
      "thickness 367\n",
      "thin 255\n",
      "think 198\n",
      "thioredoxin 258\n",
      "third 3580\n",
      "thirds 274\n",
      "thirteen 313\n",
      "thirty 896\n",
      "thomas 298\n",
      "thompson 340\n",
      "thoracic 229\n",
      "thoroughly 306\n",
      "though 2202\n",
      "thought 2853\n",
      "thousands 360\n",
      "thr 5323\n",
      "three 32288\n",
      "threefold 233\n",
      "threonine 2538\n",
      "threshold 1697\n",
      "thresholds 429\n",
      "thrombocytopenia 366\n",
      "through 209\n",
      "throughout 1845\n",
      "throughput 1288\n",
      "thumbnail 192\n",
      "thus 19809\n",
      "thymic 645\n",
      "thymidine 799\n",
      "thymine 191\n",
      "thymocytes 197\n",
      "thymus 343\n",
      "thyroid 2743\n",
      "ti 1143\n",
      "tier 200\n",
      "tiered 511\n",
      "tight 474\n",
      "tightly 692\n",
      "time 12076\n",
      "times 5193\n",
      "timing 484\n",
      "tins 205\n",
      "tion 305\n",
      "tip 431\n",
      "tissue 12518\n",
      "tissues 6266\n",
      "titer 284\n",
      "titration 432\n",
      "tk 2498\n",
      "tkb 456\n",
      "tkd 1655\n",
      "tki 5882\n",
      "tkis 5954\n",
      "tl 290\n",
      "tls 257\n",
      "tlx 197\n",
      "tm 979\n",
      "tma 414\n",
      "tmprss 1431\n",
      "tn 280\n",
      "tnbc 365\n",
      "tnf 1009\n",
      "tnrc 238\n",
      "tnt 319\n",
      "to 1835\n",
      "tobacco 235\n",
      "tof 206\n",
      "together 8793\n",
      "tokyo 711\n",
      "tolerance 616\n",
      "tolerant 246\n",
      "tolerated 2542\n",
      "tomography 670\n",
      "took 402\n",
      "tool 1356\n",
      "tools 1201\n",
      "top 5361\n",
      "topo 278\n",
      "tor 441\n",
      "torc 568\n",
      "torin 211\n",
      "total 16962\n",
      "totally 246\n",
      "toward 2779\n",
      "towards 2002\n",
      "toxic 765\n",
      "toxicities 367\n",
      "toxicity 1965\n",
      "tp 7834\n",
      "tpip 462\n",
      "tpm 397\n",
      "tpr 432\n",
      "tpte 397\n",
      "tq 226\n",
      "tr 478\n",
      "traces 320\n",
      "tract 1224\n",
      "traditional 316\n",
      "traf 345\n",
      "trafficking 559\n",
      "trail 257\n",
      "training 195\n",
      "trametinib 1168\n",
      "trans 1798\n",
      "transactivate 607\n",
      "transactivating 273\n",
      "transactivation 4820\n",
      "transcribed 964\n",
      "transcript 3573\n",
      "transcriptase 665\n",
      "transcription 17305\n",
      "transcriptional 15720\n",
      "transcriptionally 545\n",
      "transcriptome 995\n",
      "transcripts 3171\n",
      "transduced 4126\n",
      "transducer 345\n",
      "transducers 226\n",
      "transduction 3465\n",
      "transfectant 242\n",
      "transfectants 1122\n",
      "transfected 19235\n",
      "transfecting 284\n",
      "transfection 9640\n",
      "transfections 1218\n",
      "transfer 1045\n",
      "transferase 556\n",
      "transferred 2264\n",
      "transform 1293\n",
      "transformant 259\n",
      "transformants 926\n",
      "transformation 8545\n",
      "transformed 4778\n",
      "transforming 5710\n",
      "transforms 192\n",
      "transgene 910\n",
      "transgenic 2147\n",
      "transient 2264\n",
      "transiently 3233\n",
      "transition 2557\n",
      "transitions 654\n",
      "translate 214\n",
      "translated 1175\n",
      "translation 2489\n",
      "translational 1550\n",
      "translocate 306\n",
      "translocated 460\n",
      "translocates 306\n",
      "translocation 3730\n",
      "translocations 2162\n",
      "transmembrane 2380\n",
      "transmission 222\n",
      "transmitted 279\n",
      "transphosphorylation 258\n",
      "transplant 351\n",
      "transplantation 1070\n",
      "transplanted 665\n",
      "transport 478\n",
      "transversion 387\n",
      "transversions 225\n",
      "trap 381\n",
      "trastuzumab 1051\n",
      "trcn 197\n",
      "trcp 387\n",
      "tre 258\n",
      "treat 799\n",
      "treated 15896\n",
      "treating 872\n",
      "treatment 30209\n",
      "treatments 1790\n",
      "tree 869\n",
      "trend 713\n",
      "trex 301\n",
      "tri 232\n",
      "trial 3089\n",
      "trials 4825\n",
      "triangle 274\n",
      "triangles 286\n",
      "tric 1125\n",
      "trigger 640\n",
      "triggered 554\n",
      "triggering 383\n",
      "triggers 458\n",
      "trimer 272\n",
      "trimethylation 282\n",
      "trinucleotide 366\n",
      "triphosphate 707\n",
      "triple 907\n",
      "triplicate 2200\n",
      "triplicates 589\n",
      "tris 3384\n",
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      "tumors 53331\n",
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      "types 11466\n",
      "typical 1358\n",
      "typically 1662\n",
      "typing 194\n",
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      "tyrosine 20470\n",
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      "uses 712\n",
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      "usually 1653\n",
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      "uterine 766\n",
      "utility 702\n",
      "utilized 999\n",
      "utilizing 394\n",
      "utr 1319\n",
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      "validity 216\n",
      "valine 801\n",
      "valuable 585\n",
      "value 6790\n",
      "values 10254\n",
      "van 1563\n",
      "variability 1357\n",
      "variable 2560\n",
      "variables 1079\n",
      "variably 219\n",
      "variance 452\n",
      "variant 23937\n",
      "variants 66297\n",
      "variation 2474\n",
      "variations 1510\n",
      "varied 955\n",
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      "variety 3116\n",
      "various 8310\n",
      "vary 845\n",
      "varying 1321\n",
      "vascular 1135\n",
      "vast 974\n",
      "vav 198\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vb 378\n",
      "vbc 684\n",
      "vcap 501\n",
      "vcb 247\n",
      "vec 291\n",
      "vector 13902\n",
      "vectors 3141\n",
      "vegf 1072\n",
      "vegfa 686\n",
      "vegfr 1173\n",
      "vehicle 1730\n",
      "vein 219\n",
      "vemurafenib 1758\n",
      "ventricular 189\n",
      "vera 313\n",
      "verified 1762\n",
      "verify 768\n",
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      "version 3976\n",
      "versions 641\n",
      "versus 5407\n",
      "vertebrate 396\n",
      "vertebrates 197\n",
      "vertical 402\n",
      "vessels 301\n",
      "vgp 433\n",
      "vhl 6423\n",
      "vi 236\n",
      "via 6485\n",
      "viability 3332\n",
      "viable 1361\n",
      "vic 692\n",
      "vice 220\n",
      "vicinity 443\n",
      "view 5906\n",
      "viewed 215\n",
      "viewer 610\n",
      "viewerdownload 491\n",
      "views 204\n",
      "viii 409\n",
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      "viral 1525\n",
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      "virtue 224\n",
      "virus 1781\n",
      "viruses 402\n",
      "visible 528\n",
      "vismodegib 1868\n",
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      "visualized 2076\n",
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      "volumes 446\n",
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      "weight 2104\n",
      "weighted 291\n",
      "weinberg 238\n",
      "well 29171\n",
      "wells 788\n",
      "wes 581\n",
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      "wherein 333\n",
      "whether 17028\n",
      "white 1996\n",
      "whitney 486\n",
      "whole 5802\n",
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      "widely 1382\n",
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      "width 416\n",
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      "wildtype 1681\n",
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      "windows 267\n",
      "withdrawal 877\n",
      "within 22369\n",
      "without 12061\n",
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      "woman 344\n",
      "women 2381\n",
      "words 283\n",
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      "working 436\n",
      "world 584\n",
      "worldwide 868\n",
      "worse 884\n",
      "worth 385\n",
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      "wpd 1119\n",
      "written 759\n",
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      "xbai 198\n",
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      "zeiss 385\n",
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      "zo 218\n",
      "zone 311\n",
      "zr 208\n",
      "zymed 219\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Sum up the counts of each vocabulary word\n",
    "dist = np.sum(train_data_features, axis=0)\n",
    "\n",
    "# For each, print the vocabulary word and the number of times it \n",
    "# appears in the training set\n",
    "for tag, count in zip(vocab, dist):\n",
    "    print(tag, count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA - Word Text 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   ID    Gene             Variation  Class\n",
      "0   0  FAM58A  Truncating Mutations      1\n",
      "1   1     CBL                 W802*      2\n",
      "2   2     CBL                 Q249E      2\n",
      "3   3     CBL                 N454D      3\n",
      "4   4     CBL                 L399V      4\n",
      "train_v shape =  (3321, 4)\n",
      "train_v index values =  ['ID' 'Gene' 'Variation' 'Class']\n",
      "   ID     Gene Variation\n",
      "0   0    ACSL4     R570S\n",
      "1   1    NAGLU     P521L\n",
      "2   2      PAH     L333F\n",
      "3   3     ING1     A148D\n",
      "4   4  TMEM216      G77A\n",
      "test_v shape =  (5668, 3)\n",
      "test_v index values =  ['ID' 'Gene' 'Variation']\n",
      "   ID                                               Text\n",
      "0   0  Cyclin-dependent kinases (CDKs) regulate a var...\n",
      "1   1   Abstract Background  Non-small cell lung canc...\n",
      "2   2   Abstract Background  Non-small cell lung canc...\n",
      "3   3  Recent evidence has demonstrated that acquired...\n",
      "4   4  Oncogenic mutations in the monomeric Casitas B...\n",
      "train shape =  (3321, 2)\n",
      "train index values =  ['ID' 'Text']\n",
      "   ID                                               Text\n",
      "0   0  2. This mutation resulted in a myeloproliferat...\n",
      "1   1   Abstract The Large Tumor Suppressor 1 (LATS1)...\n",
      "2   2  Vascular endothelial growth factor receptor (V...\n",
      "3   3  Inflammatory myofibroblastic tumor (IMT) is a ...\n",
      "4   4   Abstract Retinoblastoma is a pediatric retina...\n",
      "test shape =  (5668, 2)\n",
      "test index values =  ['ID' 'Text']\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd       \n",
    "#train = pd.read_csv(\"genetic-data/training_text\", header=0, sep='\\|\\|', engine='python')\n",
    "train_v = pd.read_csv(\"genetic-data/training_variants\")\n",
    "test_v = pd.read_csv(\"genetic-data/test_variants\")\n",
    "train_t = pd.read_csv(\"genetic-data/training_text\", sep='\\|\\|', skiprows=1, engine='python', names=['ID', 'Text'])\n",
    "test_t = pd.read_csv(\"genetic-data/test_text\", sep='\\|\\|', skiprows=1, engine='python', names=['ID', 'Text'])\n",
    "print(train_v.head())\n",
    "print(\"train_v shape = \", train_v.shape)\n",
    "print(\"train_v index values = \", train_v.columns.values)\n",
    "print(test_v.head())\n",
    "print(\"test_v shape = \", test_v.shape)\n",
    "print(\"test_v index values = \", test_v.columns.values)\n",
    "print(train.head())\n",
    "print(\"train shape = \", train.shape)\n",
    "print(\"train index values = \", train.columns.values)\n",
    "print(test.head())\n",
    "print(\"test shape = \", test.shape)\n",
    "print(\"test index values = \", test.columns.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging test and variant data with its associated text based on ID."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "train_df = pd.merge(train_v, train_t, how='left', on='ID')\n",
    "test_df = pd.merge(test_v, test_t, how='left', on='ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gene</th>\n",
       "      <th>Variation</th>\n",
       "      <th>Class</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>FAM58A</td>\n",
       "      <td>Truncating Mutations</td>\n",
       "      <td>1</td>\n",
       "      <td>Cyclin-dependent kinases (CDKs) regulate a var...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>CBL</td>\n",
       "      <td>W802*</td>\n",
       "      <td>2</td>\n",
       "      <td>Abstract Background  Non-small cell lung canc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>CBL</td>\n",
       "      <td>Q249E</td>\n",
       "      <td>2</td>\n",
       "      <td>Abstract Background  Non-small cell lung canc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>CBL</td>\n",
       "      <td>N454D</td>\n",
       "      <td>3</td>\n",
       "      <td>Recent evidence has demonstrated that acquired...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>CBL</td>\n",
       "      <td>L399V</td>\n",
       "      <td>4</td>\n",
       "      <td>Oncogenic mutations in the monomeric Casitas B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>CBL</td>\n",
       "      <td>V391I</td>\n",
       "      <td>4</td>\n",
       "      <td>Oncogenic mutations in the monomeric Casitas B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>CBL</td>\n",
       "      <td>V430M</td>\n",
       "      <td>5</td>\n",
       "      <td>Oncogenic mutations in the monomeric Casitas B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>CBL</td>\n",
       "      <td>Deletion</td>\n",
       "      <td>1</td>\n",
       "      <td>CBL is a negative regulator of activated recep...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>CBL</td>\n",
       "      <td>Y371H</td>\n",
       "      <td>4</td>\n",
       "      <td>Abstract Juvenile myelomonocytic leukemia (JM...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>CBL</td>\n",
       "      <td>C384R</td>\n",
       "      <td>4</td>\n",
       "      <td>Abstract Juvenile myelomonocytic leukemia (JM...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>CBL</td>\n",
       "      <td>P395A</td>\n",
       "      <td>4</td>\n",
       "      <td>Oncogenic mutations in the monomeric Casitas B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>CBL</td>\n",
       "      <td>K382E</td>\n",
       "      <td>4</td>\n",
       "      <td>Noonan syndrome is an autosomal dominant conge...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>CBL</td>\n",
       "      <td>R420Q</td>\n",
       "      <td>4</td>\n",
       "      <td>Noonan syndrome is an autosomal dominant conge...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>CBL</td>\n",
       "      <td>C381A</td>\n",
       "      <td>4</td>\n",
       "      <td>Noonan syndrome is an autosomal dominant conge...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>CBL</td>\n",
       "      <td>P428L</td>\n",
       "      <td>5</td>\n",
       "      <td>Oncogenic mutations in the monomeric Casitas B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>CBL</td>\n",
       "      <td>D390Y</td>\n",
       "      <td>4</td>\n",
       "      <td>Noonan syndrome is an autosomal dominant conge...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>CBL</td>\n",
       "      <td>Truncating Mutations</td>\n",
       "      <td>1</td>\n",
       "      <td>To determine if residual cylindrical refractiv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>CBL</td>\n",
       "      <td>Q367P</td>\n",
       "      <td>4</td>\n",
       "      <td>Acquired uniparental disomy (aUPD) is a common...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>CBL</td>\n",
       "      <td>M374V</td>\n",
       "      <td>5</td>\n",
       "      <td>Oncogenic mutations in the monomeric Casitas B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>CBL</td>\n",
       "      <td>Y371S</td>\n",
       "      <td>4</td>\n",
       "      <td>Acquired uniparental disomy (aUPD) is a common...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID    Gene             Variation  Class  \\\n",
       "0    0  FAM58A  Truncating Mutations      1   \n",
       "1    1     CBL                 W802*      2   \n",
       "2    2     CBL                 Q249E      2   \n",
       "3    3     CBL                 N454D      3   \n",
       "4    4     CBL                 L399V      4   \n",
       "5    5     CBL                 V391I      4   \n",
       "6    6     CBL                 V430M      5   \n",
       "7    7     CBL              Deletion      1   \n",
       "8    8     CBL                 Y371H      4   \n",
       "9    9     CBL                 C384R      4   \n",
       "10  10     CBL                 P395A      4   \n",
       "11  11     CBL                 K382E      4   \n",
       "12  12     CBL                 R420Q      4   \n",
       "13  13     CBL                 C381A      4   \n",
       "14  14     CBL                 P428L      5   \n",
       "15  15     CBL                 D390Y      4   \n",
       "16  16     CBL  Truncating Mutations      1   \n",
       "17  17     CBL                 Q367P      4   \n",
       "18  18     CBL                 M374V      5   \n",
       "19  19     CBL                 Y371S      4   \n",
       "\n",
       "                                                 Text  \n",
       "0   Cyclin-dependent kinases (CDKs) regulate a var...  \n",
       "1    Abstract Background  Non-small cell lung canc...  \n",
       "2    Abstract Background  Non-small cell lung canc...  \n",
       "3   Recent evidence has demonstrated that acquired...  \n",
       "4   Oncogenic mutations in the monomeric Casitas B...  \n",
       "5   Oncogenic mutations in the monomeric Casitas B...  \n",
       "6   Oncogenic mutations in the monomeric Casitas B...  \n",
       "7   CBL is a negative regulator of activated recep...  \n",
       "8    Abstract Juvenile myelomonocytic leukemia (JM...  \n",
       "9    Abstract Juvenile myelomonocytic leukemia (JM...  \n",
       "10  Oncogenic mutations in the monomeric Casitas B...  \n",
       "11  Noonan syndrome is an autosomal dominant conge...  \n",
       "12  Noonan syndrome is an autosomal dominant conge...  \n",
       "13  Noonan syndrome is an autosomal dominant conge...  \n",
       "14  Oncogenic mutations in the monomeric Casitas B...  \n",
       "15  Noonan syndrome is an autosomal dominant conge...  \n",
       "16  To determine if residual cylindrical refractiv...  \n",
       "17  Acquired uniparental disomy (aUPD) is a common...  \n",
       "18  Oncogenic mutations in the monomeric Casitas B...  \n",
       "19  Acquired uniparental disomy (aUPD) is a common...  "
      ]
     },
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       "      <th>ID</th>\n",
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       "      <td>2. This mutation resulted in a myeloproliferat...</td>\n",
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       "      <th>5</th>\n",
       "      <td>5</td>\n",
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       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>KLF11</td>\n",
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       "      <td>Aberrations in the mTOR (mechanistic target of...</td>\n",
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       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>SGCB</td>\n",
       "      <td>T151R</td>\n",
       "      <td>Oncogenic mutations in the monomeric Casitas B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>CLCF1</td>\n",
       "      <td>R197L</td>\n",
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       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>SPTLC2</td>\n",
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       "      <td>The protein kinase B-RAF is mutated in approxi...</td>\n",
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       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>SUMF1</td>\n",
       "      <td>A348P</td>\n",
       "      <td>Genes of the Raf family encode kinases that ar...</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>TET2</td>\n",
       "      <td>Y1902A</td>\n",
       "      <td>TET proteins oxidize 5-methylcytosine (5mC) on...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>G6PD</td>\n",
       "      <td>D312H</td>\n",
       "      <td>The p110δ subunit of phosphoinositide 3-kinase...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>SNCB</td>\n",
       "      <td>P123H</td>\n",
       "      <td>Abstract  Purpose: Positron emission tomograp...</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>EFNB1</td>\n",
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       "      <td>Mutations leading to activation of the RAF-mit...</td>\n",
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       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>PKHD1</td>\n",
       "      <td>V3471G</td>\n",
       "      <td>Janus (Jak) tyrosine kinases contain a tyrosin...</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>F8</td>\n",
       "      <td>R2323H</td>\n",
       "      <td>Germ line missense mutations in HRAS and KRAS ...</td>\n",
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       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>PGK1</td>\n",
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       "      <td>The actions of RhoA in cytoskeletal regulation...</td>\n",
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       "    ID     Gene Variation                                               Text\n",
       "0    0    ACSL4     R570S  2. This mutation resulted in a myeloproliferat...\n",
       "1    1    NAGLU     P521L   Abstract The Large Tumor Suppressor 1 (LATS1)...\n",
       "2    2      PAH     L333F  Vascular endothelial growth factor receptor (V...\n",
       "3    3     ING1     A148D  Inflammatory myofibroblastic tumor (IMT) is a ...\n",
       "4    4  TMEM216      G77A   Abstract Retinoblastoma is a pediatric retina...\n",
       "5    5   CD40LG     A123E  The accurate determination of perfluoroalkyl s...\n",
       "6    6    KLF11     T220M  Aberrations in the mTOR (mechanistic target of...\n",
       "7    7     SGCB     T151R  Oncogenic mutations in the monomeric Casitas B...\n",
       "8    8    CLCF1     R197L  NPM1 gene at chromosome 5q35 is involved in re...\n",
       "9    9   SDHAF1      R55P  Introduction The epidermal growth factor recep...\n",
       "10  10   SPTLC2     I504F  The protein kinase B-RAF is mutated in approxi...\n",
       "11  11    SUMF1     A348P  Genes of the Raf family encode kinases that ar...\n",
       "12  12     TET2    Y1902A  TET proteins oxidize 5-methylcytosine (5mC) on...\n",
       "13  13     G6PD     D312H  The p110δ subunit of phosphoinositide 3-kinase...\n",
       "14  14     SNCB     P123H   Abstract  Purpose: Positron emission tomograp...\n",
       "15  15    EFNB1     M158V  Mutations leading to activation of the RAF-mit...\n",
       "16  16    PKHD1    V3471G  Janus (Jak) tyrosine kinases contain a tyrosin...\n",
       "17  17       F8    R2323H  Germ line missense mutations in HRAS and KRAS ...\n",
       "18  18     PGK1     T352N  The actions of RhoA in cytoskeletal regulation...\n",
       "19  19     MTOR    D2512H  Genes encoding components of the PI3K-Akt-mTOR..."
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