Copy_of_drugbank_analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of drugbank_analysis.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Fhrn4sse7-Q"
      },
      "source": [
        "import pandas as pd\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "Ly8gQ29weXJpZ2h0IDIwMTcgR29vZ2xlIExMQwovLwovLyBMaWNlbnNlZCB1bmRlciB0aGUgQXBhY2hlIExpY2Vuc2UsIFZlcnNpb24gMi4wICh0aGUgIkxpY2Vuc2UiKTsKLy8geW91IG1heSBub3QgdXNlIHRoaXMgZmlsZSBleGNlcHQgaW4gY29tcGxpYW5jZSB3aXRoIHRoZSBMaWNlbnNlLgovLyBZb3UgbWF5IG9idGFpbiBhIGNvcHkgb2YgdGhlIExpY2Vuc2UgYXQKLy8KLy8gICAgICBodHRwOi8vd3d3LmFwYWNoZS5vcmcvbGljZW5zZXMvTElDRU5TRS0yLjAKLy8KLy8gVW5sZXNzIHJlcXVpcmVkIGJ5IGFwcGxpY2FibGUgbGF3IG9yIGFncmVlZCB0byBpbiB3cml0aW5nLCBzb2Z0d2FyZQovLyBkaXN0cmlidXRlZCB1bmRlciB0aGUgTGljZW5zZSBpcyBkaXN0cmlidXRlZCBvbiBhbiAiQVMgSVMiIEJBU0lTLAovLyBXSVRIT1VUIFdBUlJBTlRJRVMgT1IgQ09ORElUSU9OUyBPRiBBTlkgS0lORCwgZWl0aGVyIGV4cHJlc3Mgb3IgaW1wbGllZC4KLy8gU2VlIHRoZSBMaWNlbnNlIGZvciB0aGUgc3BlY2lmaWMgbGFuZ3VhZ2UgZ292ZXJuaW5nIHBlcm1pc3Npb25zIGFuZAovLyBsaW1pdGF0aW9ucyB1bmRlciB0aGUgTGljZW5zZS4KCi8qKgogKiBAZmlsZW92ZXJ2aWV3IEhlbHBlcnMgZm9yIGdvb2dsZS5jb2xhYiBQeXRob24gbW9kdWxlLgogKi8KKGZ1bmN0aW9uKHNjb3BlKSB7CmZ1bmN0aW9uIHNwYW4odGV4dCwgc3R5bGVBdHRyaWJ1dGVzID0ge30pIHsKICBjb25zdCBlbGVtZW50ID0gZG9jdW1lbnQuY3JlYXRlRWxlbWVudCgnc3BhbicpOwogIGVsZW1lbnQudGV4dENvbnRlbnQgPSB0ZXh0OwogIGZvciAoY29uc3Qga2V5IG9mIE9iamVjdC5rZXlzKHN0eWxlQXR0cmlidXRlcykpIHsKICAgIGVsZW1lbnQuc3R5bGVba2V5XSA9IHN0eWxlQXR0cmlidXRlc1trZXldOwogIH0KICByZXR1cm4gZWxlbWVudDsKfQoKLy8gTWF4IG51bWJlciBvZiBieXRlcyB3aGljaCB3aWxsIGJlIHVwbG9hZGVkIGF0IGEgdGltZS4KY29uc3QgTUFYX1BBWUxPQURfU0laRSA9IDEwMCAqIDEwMjQ7CgpmdW5jdGlvbiBfdXBsb2FkRmlsZXMoaW5wdXRJZCwgb3V0cHV0SWQpIHsKICBjb25zdCBzdGVwcyA9IHVwbG9hZEZpbGVzU3RlcChpbnB1dElkLCBvdXRwdXRJZCk7CiAgY29uc3Qgb3V0cHV0RWxlbWVudCA9IGRvY3VtZW50LmdldEVsZW1lbnRCeUlkKG91dHB1dElkKTsKICAvLyBDYWNoZSBzdGVwcyBvbiB0aGUgb3V0cHV0RWxlbWVudCB0byBtYWtlIGl0IGF2YWlsYWJsZSBmb3IgdGhlIG5leHQgY2FsbAogIC8vIHRvIHVwbG9hZEZpbGVzQ29udGludWUgZnJvbSBQeXRob24uCiAgb3V0cHV0RWxlbWVudC5zdGVwcyA9IHN0ZXBzOwoKICByZXR1cm4gX3VwbG9hZEZpbGVzQ29udGludWUob3V0cHV0SWQpOwp9CgovLyBUaGlzIGlzIHJvdWdobHkgYW4gYXN5bmMgZ2VuZXJhdG9yIChub3Qgc3VwcG9ydGVkIGluIHRoZSBicm93c2VyIHlldCksCi8vIHdoZXJlIHRoZXJlIGFyZSBtdWx0aXBsZSBhc3luY2hyb25vdXMgc3RlcHMgYW5kIHRoZSBQeXRob24gc2lkZSBpcyBnb2luZwovLyB0byBwb2xsIGZvciBjb21wbGV0aW9uIG9mIGVhY2ggc3RlcC4KLy8gVGhpcyB1c2VzIGEgUHJvbWlzZSB0byBibG9jayB0aGUgcHl0aG9uIHNpZGUgb24gY29tcGxldGlvbiBvZiBlYWNoIHN0ZXAsCi8vIHRoZW4gcGFzc2VzIHRoZSByZXN1bHQgb2YgdGhlIHByZXZpb3VzIHN0ZXAgYXMgdGhlIGlucHV0IHRvIHRoZSBuZXh0IHN0ZXAuCmZ1bmN0aW9uIF91cGxvYWRGaWxlc0NvbnRpbnVlKG91dHB1dElkKSB7CiAgY29uc3Qgb3V0cHV0RWxlbWVudCA9IGRvY3VtZW50LmdldEVsZW1lbnRCeUlkKG91dHB1dElkKTsKICBjb25zdCBzdGVwcyA9IG91dHB1dEVsZW1lbnQuc3RlcHM7CgogIGNvbnN0IG5leHQgPSBzdGVwcy5uZXh0KG91dHB1dEVsZW1lbnQubGFzdFByb21pc2VWYWx1ZSk7CiAgcmV0dXJuIFByb21pc2UucmVzb2x2ZShuZXh0LnZhbHVlLnByb21pc2UpLnRoZW4oKHZhbHVlKSA9PiB7CiAgICAvLyBDYWNoZSB0aGUgbGFzdCBwcm9taXNlIHZhbHVlIHRvIG1ha2UgaXQgYXZhaWxhYmxlIHRvIHRoZSBuZXh0CiAgICAvLyBzdGVwIG9mIHRoZSBnZW5lcmF0b3IuCiAgICBvdXRwdXRFbGVtZW50Lmxhc3RQcm9taXNlVmFsdWUgPSB2YWx1ZTsKICAgIHJldHVybiBuZXh0LnZhbHVlLnJlc3BvbnNlOwogIH0pOwp9CgovKioKICogR2VuZXJhdG9yIGZ1bmN0aW9uIHdoaWNoIGlzIGNhbGxlZCBiZXR3ZWVuIGVhY2ggYXN5bmMgc3RlcCBvZiB0aGUgdXBsb2FkCiAqIHByb2Nlc3MuCiAqIEBwYXJhbSB7c3RyaW5nfSBpbnB1dElkIEVsZW1lbnQgSUQgb2YgdGhlIGlucHV0IGZpbGUgcGlja2VyIGVsZW1lbnQuCiAqIEBwYXJhbSB7c3RyaW5nfSBvdXRwdXRJZCBFbGVtZW50IElEIG9mIHRoZSBvdXRwdXQgZGlzcGxheS4KICogQHJldHVybiB7IUl0ZXJhYmxlPCFPYmplY3Q+fSBJdGVyYWJsZSBvZiBuZXh0IHN0ZXBzLgogKi8KZnVuY3Rpb24qIHVwbG9hZEZpbGVzU3RlcChpbnB1dElkLCBvdXRwdXRJZCkgewogIGNvbnN0IGlucHV0RWxlbWVudCA9IGRvY3VtZW50LmdldEVsZW1lbnRCeUlkKGlucHV0SWQpOwogIGlucHV0RWxlbWVudC5kaXNhYmxlZCA9IGZhbHNlOwoKICBjb25zdCBvdXRwdXRFbGVtZW50ID0gZG9jdW1lbnQuZ2V0RWxlbWVudEJ5SWQob3V0cHV0SWQpOwogIG91dHB1dEVsZW1lbnQuaW5uZXJIVE1MID0gJyc7CgogIGNvbnN0IHBpY2tlZFByb21pc2UgPSBuZXcgUHJvbWlzZSgocmVzb2x2ZSkgPT4gewogICAgaW5wdXRFbGVtZW50LmFkZEV2ZW50TGlzdGVuZXIoJ2NoYW5nZScsIChlKSA9PiB7CiAgICAgIHJlc29sdmUoZS50YXJnZXQuZmlsZXMpOwogICAgfSk7CiAgfSk7CgogIGNvbnN0IGNhbmNlbCA9IGRvY3VtZW50LmNyZWF0ZUVsZW1lbnQoJ2J1dHRvbicpOwogIGlucHV0RWxlbWVudC5wYXJlbnRFbGVtZW50LmFwcGVuZENoaWxkKGNhbmNlbCk7CiAgY2FuY2VsLnRleHRDb250ZW50ID0gJ0NhbmNlbCB1cGxvYWQnOwogIGNvbnN0IGNhbmNlbFByb21pc2UgPSBuZXcgUHJvbWlzZSgocmVzb2x2ZSkgPT4gewogICAgY2FuY2VsLm9uY2xpY2sgPSAoKSA9PiB7CiAgICAgIHJlc29sdmUobnVsbCk7CiAgICB9OwogIH0pOwoKICAvLyBXYWl0IGZvciB0aGUgdXNlciB0byBwaWNrIHRoZSBmaWxlcy4KICBjb25zdCBmaWxlcyA9IHlpZWxkIHsKICAgIHByb21pc2U6IFByb21pc2UucmFjZShbcGlja2VkUHJvbWlzZSwgY2FuY2VsUHJvbWlzZV0pLAogICAgcmVzcG9uc2U6IHsKICAgICAgYWN0aW9uOiAnc3RhcnRpbmcnLAogICAgfQogIH07CgogIGNhbmNlbC5yZW1vdmUoKTsKCiAgLy8gRGlzYWJsZSB0aGUgaW5wdXQgZWxlbWVudCBzaW5jZSBmdXJ0aGVyIHBpY2tzIGFyZSBub3QgYWxsb3dlZC4KICBpbnB1dEVsZW1lbnQuZGlzYWJsZWQgPSB0cnVlOwoKICBpZiAoIWZpbGVzKSB7CiAgICByZXR1cm4gewogICAgICByZXNwb25zZTogewogICAgICAgIGFjdGlvbjogJ2NvbXBsZXRlJywKICAgICAgfQogICAgfTsKICB9CgogIGZvciAoY29uc3QgZmlsZSBvZiBmaWxlcykgewogICAgY29uc3QgbGkgPSBkb2N1bWVudC5jcmVhdGVFbGVtZW50KCdsaScpOwogICAgbGkuYXBwZW5kKHNwYW4oZmlsZS5uYW1lLCB7Zm9udFdlaWdodDogJ2JvbGQnfSkpOwogICAgbGkuYXBwZW5kKHNwYW4oCiAgICAgICAgYCgke2ZpbGUudHlwZSB8fCAnbi9hJ30pIC0gJHtmaWxlLnNpemV9IGJ5dGVzLCBgICsKICAgICAgICBgbGFzdCBtb2RpZmllZDogJHsKICAgICAgICAgICAgZmlsZS5sYXN0TW9kaWZpZWREYXRlID8gZmlsZS5sYXN0TW9kaWZpZWREYXRlLnRvTG9jYWxlRGF0ZVN0cmluZygpIDoKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgJ24vYSd9IC0gYCkpOwogICAgY29uc3QgcGVyY2VudCA9IHNwYW4oJzAlIGRvbmUnKTsKICAgIGxpLmFwcGVuZENoaWxkKHBlcmNlbnQpOwoKICAgIG91dHB1dEVsZW1lbnQuYXBwZW5kQ2hpbGQobGkpOwoKICAgIGNvbnN0IGZpbGVEYXRhUHJvbWlzZSA9IG5ldyBQcm9taXNlKChyZXNvbHZlKSA9PiB7CiAgICAgIGNvbnN0IHJlYWRlciA9IG5ldyBGaWxlUmVhZGVyKCk7CiAgICAgIHJlYWRlci5vbmxvYWQgPSAoZSkgPT4gewogICAgICAgIHJlc29sdmUoZS50YXJnZXQucmVzdWx0KTsKICAgICAgfTsKICAgICAgcmVhZGVyLnJlYWRBc0FycmF5QnVmZmVyKGZpbGUpOwogICAgfSk7CiAgICAvLyBXYWl0IGZvciB0aGUgZGF0YSB0byBiZSByZWFkeS4KICAgIGxldCBmaWxlRGF0YSA9IHlpZWxkIHsKICAgICAgcHJvbWlzZTogZmlsZURhdGFQcm9taXNlLAogICAgICByZXNwb25zZTogewogICAgICAgIGFjdGlvbjogJ2NvbnRpbnVlJywKICAgICAgfQogICAgfTsKCiAgICAvLyBVc2UgYSBjaHVua2VkIHNlbmRpbmcgdG8gYXZvaWQgbWVzc2FnZSBzaXplIGxpbWl0cy4gU2VlIGIvNjIxMTU2NjAuCiAgICBsZXQgcG9zaXRpb24gPSAwOwogICAgd2hpbGUgKHBvc2l0aW9uIDwgZmlsZURhdGEuYnl0ZUxlbmd0aCkgewogICAgICBjb25zdCBsZW5ndGggPSBNYXRoLm1pbihmaWxlRGF0YS5ieXRlTGVuZ3RoIC0gcG9zaXRpb24sIE1BWF9QQVlMT0FEX1NJWkUpOwogICAgICBjb25zdCBjaHVuayA9IG5ldyBVaW50OEFycmF5KGZpbGVEYXRhLCBwb3NpdGlvbiwgbGVuZ3RoKTsKICAgICAgcG9zaXRpb24gKz0gbGVuZ3RoOwoKICAgICAgY29uc3QgYmFzZTY0ID0gYnRvYShTdHJpbmcuZnJvbUNoYXJDb2RlLmFwcGx5KG51bGwsIGNodW5rKSk7CiAgICAgIHlpZWxkIHsKICAgICAgICByZXNwb25zZTogewogICAgICAgICAgYWN0aW9uOiAnYXBwZW5kJywKICAgICAgICAgIGZpbGU6IGZpbGUubmFtZSwKICAgICAgICAgIGRhdGE6IGJhc2U2NCwKICAgICAgICB9LAogICAgICB9OwogICAgICBwZXJjZW50LnRleHRDb250ZW50ID0KICAgICAgICAgIGAke01hdGgucm91bmQoKHBvc2l0aW9uIC8gZmlsZURhdGEuYnl0ZUxlbmd0aCkgKiAxMDApfSUgZG9uZWA7CiAgICB9CiAgfQoKICAvLyBBbGwgZG9uZS4KICB5aWVsZCB7CiAgICByZXNwb25zZTogewogICAgICBhY3Rpb246ICdjb21wbGV0ZScsCiAgICB9CiAgfTsKfQoKc2NvcGUuZ29vZ2xlID0gc2NvcGUuZ29vZ2xlIHx8IHt9OwpzY29wZS5nb29nbGUuY29sYWIgPSBzY29wZS5nb29nbGUuY29sYWIgfHwge307CnNjb3BlLmdvb2dsZS5jb2xhYi5fZmlsZXMgPSB7CiAgX3VwbG9hZEZpbGVzLAogIF91cGxvYWRGaWxlc0NvbnRpbnVlLAp9Owp9KShzZWxmKTsK",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "lwTW1GjqfeIT",
        "outputId": "eaefa7dd-5e32-4013-b662-6348af4bd843"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-8bdac1f2-8a31-4cf8-824f-dbd53bc31caf\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-8bdac1f2-8a31-4cf8-824f-dbd53bc31caf\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving drugbank-human.csv to drugbank-human (1).csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y_7vWFUVfqVv"
      },
      "source": [
        "dados_drugbank = pd.read_csv('drugbank-human (1).csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "NMvSlegpfyL3",
        "outputId": "46b12b9a-75d0-4f2a-dfff-ae97ddbf5aa6"
      },
      "source": [
        "dados_drugbank.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GSECODE</th>\n",
              "      <th>MPS_TYPE</th>\n",
              "      <th>Symbol</th>\n",
              "      <th>DRUGBANK</th>\n",
              "      <th>EXPRESSION</th>\n",
              "      <th>MOA</th>\n",
              "      <th>REF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>EEF1A1P11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>RPL10P9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>ZBTB12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>MRPL45</td>\n",
              "      <td>Copper</td>\n",
              "      <td>DOWN</td>\n",
              "      <td>[Disulfiram binds to Copper] which results in ...</td>\n",
              "      <td>https://pubmed.ncbi.nlm.nih.gov/24690739/</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>MRPL45</td>\n",
              "      <td>Disulfiram</td>\n",
              "      <td>DOWN</td>\n",
              "      <td>[Disulfiram binds to Copper] which results in ...</td>\n",
              "      <td>https://pubmed.ncbi.nlm.nih.gov/24690739/</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     GSECODE  ...                                        REF\n",
              "0  GSE111906  ...                                        NaN\n",
              "1  GSE111906  ...                                        NaN\n",
              "2  GSE111906  ...                                        NaN\n",
              "3  GSE111906  ...  https://pubmed.ncbi.nlm.nih.gov/24690739/\n",
              "4  GSE111906  ...  https://pubmed.ncbi.nlm.nih.gov/24690739/\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9s0ZRd7HgiNb",
        "outputId": "d00a7d25-bdf0-4095-a4b5-7287918e1993"
      },
      "source": [
        "dados_drugbank.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(926, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "duxMzEUjTyoc",
        "outputId": "471418cb-f2f5-4062-c7e2-4cbfe242cc5b"
      },
      "source": [
        "dados_drugbank.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GSECODE</th>\n",
              "      <th>MPS TYPE</th>\n",
              "      <th>Symbol</th>\n",
              "      <th>DRUGBANK</th>\n",
              "      <th>EXPRESSION</th>\n",
              "      <th>MOA</th>\n",
              "      <th>REF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>EEF1A1P11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>RPL10P9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>ZBTB12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>MRPL45</td>\n",
              "      <td>Copper</td>\n",
              "      <td>DOWN</td>\n",
              "      <td>[Disulfiram binds to Copper] which results in ...</td>\n",
              "      <td>https://pubmed.ncbi.nlm.nih.gov/24690739/</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>MRPL45</td>\n",
              "      <td>Disulfiram</td>\n",
              "      <td>DOWN</td>\n",
              "      <td>[Disulfiram binds to Copper] which results in ...</td>\n",
              "      <td>https://pubmed.ncbi.nlm.nih.gov/24690739/</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     GSECODE  ...                                        REF\n",
              "0  GSE111906  ...                                        NaN\n",
              "1  GSE111906  ...                                        NaN\n",
              "2  GSE111906  ...                                        NaN\n",
              "3  GSE111906  ...  https://pubmed.ncbi.nlm.nih.gov/24690739/\n",
              "4  GSE111906  ...  https://pubmed.ncbi.nlm.nih.gov/24690739/\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5JA_ePNSUbP6",
        "outputId": "fd61b1e7-a170-403a-ccb3-472e358a7251"
      },
      "source": [
        "dados_drugbank.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(926, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y1w7r0luaNDc"
      },
      "source": [
        "cod_drugbankgenes= dados_drugbank['Symbol'].value_counts().index[0:10]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5E0STd2faVAt",
        "outputId": "8c580306-c5ec-4a2b-8e38-4de51d19fd80"
      },
      "source": [
        "cod_drugbankgenes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['DDIT3', 'ASNS', 'IGFBP3', 'ID3', 'PTGS1', 'S100P', 'BHLHE40',\n",
              "       'TPD52L1', 'ITPR1', 'PDK1'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "LPnDpy-naas-",
        "outputId": "eadeb8e7-cd14-4760-ac69-d57d4ca1fad2"
      },
      "source": [
        "dados_drugbank.query('Symbol in @cod_drugbankgenes')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GSECODE</th>\n",
              "      <th>MPS TYPE</th>\n",
              "      <th>Symbol</th>\n",
              "      <th>DRUGBANK</th>\n",
              "      <th>EXPRESSION</th>\n",
              "      <th>MOA</th>\n",
              "      <th>REF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>ID3</td>\n",
              "      <td>Stanolone</td>\n",
              "      <td>UP</td>\n",
              "      <td>Dihydrotestosterone results in increased expre...</td>\n",
              "      <td>https://pubmed.ncbi.nlm.nih.gov/16541417/</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>ID3</td>\n",
              "      <td>Arachidonic Acid</td>\n",
              "      <td>DOWN</td>\n",
              "      <td>Arachidonic Acid results in decreased expressi...</td>\n",
              "      <td>https://pubmed.ncbi.nlm.nih.gov/16704987/</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>ID3</td>\n",
              "      <td>Azacitidine</td>\n",
              "      <td>UP</td>\n",
              "      <td>Azacitidine results in increased expression of...</td>\n",
              "      <td>https://pubmed.ncbi.nlm.nih.gov/19194470/</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>ID3</td>\n",
              "      <td>Azathioprine</td>\n",
              "      <td>UP</td>\n",
              "      <td>Azathioprine results in increased expression o...</td>\n",
              "      <td>https://pubmed.ncbi.nlm.nih.gov/19192274/</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>ID3</td>\n",
              "      <td>Estradiol</td>\n",
              "      <td>DOWN</td>\n",
              "      <td>Estradiol results in decreased expression of I...</td>\n",
              "      <td>https://pubmed.ncbi.nlm.nih.gov/21185374/ http...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>921</th>\n",
              "      <td>GSE32154</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>S100P</td>\n",
              "      <td>Silicon dioxide</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Silicon Dioxide analog results in decreased ex...</td>\n",
              "      <td>A24195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>922</th>\n",
              "      <td>GSE32154</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>S100P</td>\n",
              "      <td>Testosterone</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Testosterone results in increased expression o...</td>\n",
              "      <td>A22301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>923</th>\n",
              "      <td>GSE32154</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>S100P</td>\n",
              "      <td>Tretinoin</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tretinoin results in increased expression of S...</td>\n",
              "      <td>A24455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>924</th>\n",
              "      <td>GSE32154</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>S100P</td>\n",
              "      <td>Valproic acid</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Valproic Acid results in increased expression ...</td>\n",
              "      <td>A21035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>925</th>\n",
              "      <td>GSE32154</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>S100P</td>\n",
              "      <td>Zinc sulfate</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Zinc Sulfate results in increased expression o...</td>\n",
              "      <td>A24818</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>409 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       GSECODE  ...                                                REF\n",
              "58   GSE111906  ...          https://pubmed.ncbi.nlm.nih.gov/16541417/\n",
              "59   GSE111906  ...          https://pubmed.ncbi.nlm.nih.gov/16704987/\n",
              "60   GSE111906  ...          https://pubmed.ncbi.nlm.nih.gov/19194470/\n",
              "61   GSE111906  ...          https://pubmed.ncbi.nlm.nih.gov/19192274/\n",
              "62   GSE111906  ...  https://pubmed.ncbi.nlm.nih.gov/21185374/ http...\n",
              "..         ...  ...                                                ...\n",
              "921   GSE32154  ...                                             A24195\n",
              "922   GSE32154  ...                                             A22301\n",
              "923   GSE32154  ...                                             A24455\n",
              "924   GSE32154  ...                                             A21035\n",
              "925   GSE32154  ...                                             A24818\n",
              "\n",
              "[409 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "zvAnBdGl_r9u",
        "outputId": "a74ed558-3724-4b9d-b8d3-7e363335ec09"
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "sns.set()\n",
        "plt.figure(figsize=(35, 12))\n",
        "ax = sns.countplot(x = 'Symbol', data=dados_drugbank.query('Symbol in @cod_drugbankgenes'), order= dados_drugbank['Symbol'].value_counts().index[0:10], palette='husl')\n",
        "ax.set_title('Top genes')\n",
        "plt.title(\"Top genes\", fontsize = 20)\n",
        "plt.xlabel(\"genes\", fontsize = 18)\n",
        "plt.ylabel(\"drugs\", fontsize = 18)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 2520x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vjX9TLG6CHle"
      },
      "source": [
        "cod_drugs = dados_drugbank['DRUGBANK'].value_counts().index[0:10]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l73ENFUBCPuq",
        "outputId": "9b9cc2b9-afeb-48d1-c383-853eea8f40d8"
      },
      "source": [
        "cod_drugs"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Valproic acid', 'Cyclosporine', 'Silicon dioxide', 'Tretinoin',\n",
              "       'Estradiol', 'Quercetin', 'Formaldehyde', 'Acetaminophen', 'Entinostat',\n",
              "       'Calcitriol'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "0jEAP46uCWPc",
        "outputId": "144db233-2d88-40bd-facb-8b19b1122732"
      },
      "source": [
        "dados_drugbank.query('DRUGBANK in @cod_drugs')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GSECODE</th>\n",
              "      <th>MPS TYPE</th>\n",
              "      <th>Symbol</th>\n",
              "      <th>DRUGBANK</th>\n",
              "      <th>EXPRESSION</th>\n",
              "      <th>MOA</th>\n",
              "      <th>REF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>MRPL45</td>\n",
              "      <td>Valproic acid</td>\n",
              "      <td>DOWN</td>\n",
              "      <td>Valproic Acid results in decreased expression ...</td>\n",
              "      <td>https://pubmed.ncbi.nlm.nih.gov/23179753/</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>OTX1</td>\n",
              "      <td>Silicon dioxide</td>\n",
              "      <td>DOWN</td>\n",
              "      <td>Silicon Dioxide analog results in decreased ex...</td>\n",
              "      <td>https://pubmed.ncbi.nlm.nih.gov/25895662/</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>OTX1</td>\n",
              "      <td>Valproic acid</td>\n",
              "      <td>DOWN</td>\n",
              "      <td>Valproic Acid results in decreased expression ...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>ZNRD1</td>\n",
              "      <td>Quercetin</td>\n",
              "      <td>DOWN</td>\n",
              "      <td>Quercetin results in decreased expression of Z...</td>\n",
              "      <td>https://clincancerres.aacrjournals.org/content...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>ZNRD1</td>\n",
              "      <td>Valproic acid</td>\n",
              "      <td>DOWN</td>\n",
              "      <td>Valproic Acid results in decreased expression ...</td>\n",
              "      <td>https://pubmed.ncbi.nlm.nih.gov/27188386/</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>910</th>\n",
              "      <td>GSE32154</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>S100P</td>\n",
              "      <td>Entinostat</td>\n",
              "      <td>NaN</td>\n",
              "      <td>entinostat results in increased expression of ...</td>\n",
              "      <td>A21035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>919</th>\n",
              "      <td>GSE32154</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>S100P</td>\n",
              "      <td>Quercetin</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Quercetin results in decreased expression of S...</td>\n",
              "      <td>A23737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>921</th>\n",
              "      <td>GSE32154</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>S100P</td>\n",
              "      <td>Silicon dioxide</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Silicon Dioxide analog results in decreased ex...</td>\n",
              "      <td>A24195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>923</th>\n",
              "      <td>GSE32154</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>S100P</td>\n",
              "      <td>Tretinoin</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tretinoin results in increased expression of S...</td>\n",
              "      <td>A24455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>924</th>\n",
              "      <td>GSE32154</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>S100P</td>\n",
              "      <td>Valproic acid</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Valproic Acid results in increased expression ...</td>\n",
              "      <td>A21035</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>331 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       GSECODE  ...                                                REF\n",
              "6    GSE111906  ...          https://pubmed.ncbi.nlm.nih.gov/23179753/\n",
              "10   GSE111906  ...          https://pubmed.ncbi.nlm.nih.gov/25895662/\n",
              "11   GSE111906  ...                                                NaN\n",
              "15   GSE111906  ...  https://clincancerres.aacrjournals.org/content...\n",
              "16   GSE111906  ...          https://pubmed.ncbi.nlm.nih.gov/27188386/\n",
              "..         ...  ...                                                ...\n",
              "910   GSE32154  ...                                             A21035\n",
              "919   GSE32154  ...                                             A23737\n",
              "921   GSE32154  ...                                             A24195\n",
              "923   GSE32154  ...                                             A24455\n",
              "924   GSE32154  ...                                             A21035\n",
              "\n",
              "[331 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 766
        },
        "id": "Mw8ByfvDCdXS",
        "outputId": "f079a717-c655-499f-d7eb-fc6116e29cee"
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "sns.set()\n",
        "plt.figure(figsize=(35, 12))\n",
        "ax = sns.countplot(x = 'DRUGBANK', data=dados_drugbank.query('DRUGBANK in @cod_drugs'), order= dados_drugbank['DRUGBANK'].value_counts().index[0:10], palette='husl')\n",
        "ax.set_title('Top drugs')\n",
        "plt.title(\"Top drugs\", fontsize = 20)\n",
        "plt.xlabel(\"Top drugs\", fontsize = 18)\n",
        "plt.ylabel(\"genes\", fontsize = 18)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 2520x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1BvVktWnPoHE"
      },
      "source": [
        "cod_gse= dados_drugbank['GSECODE'].value_counts().index[0:10]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "77MQ1MdQPpEr",
        "outputId": "dadc8f1a-3ed3-4872-cbf2-385f4a6653ab"
      },
      "source": [
        "cod_gse"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['GSE23075', 'GSE32154', 'GSE111906'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "Cgqj-f1hPpPK",
        "outputId": "e50f387f-0b5d-480a-b49e-7d476d104e07"
      },
      "source": [
        "dados_drugbank.query('GSECODE in @cod_gse')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GSECODE</th>\n",
              "      <th>MPS TYPE</th>\n",
              "      <th>Symbol</th>\n",
              "      <th>DRUGBANK</th>\n",
              "      <th>EXPRESSION</th>\n",
              "      <th>MOA</th>\n",
              "      <th>REF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>EEF1A1P11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>RPL10P9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>ZBTB12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>MRPL45</td>\n",
              "      <td>Copper</td>\n",
              "      <td>DOWN</td>\n",
              "      <td>[Disulfiram binds to Copper] which results in ...</td>\n",
              "      <td>https://pubmed.ncbi.nlm.nih.gov/24690739/</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>MRPL45</td>\n",
              "      <td>Disulfiram</td>\n",
              "      <td>DOWN</td>\n",
              "      <td>[Disulfiram binds to Copper] which results in ...</td>\n",
              "      <td>https://pubmed.ncbi.nlm.nih.gov/24690739/</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>921</th>\n",
              "      <td>GSE32154</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>S100P</td>\n",
              "      <td>Silicon dioxide</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Silicon Dioxide analog results in decreased ex...</td>\n",
              "      <td>A24195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>922</th>\n",
              "      <td>GSE32154</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>S100P</td>\n",
              "      <td>Testosterone</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Testosterone results in increased expression o...</td>\n",
              "      <td>A22301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>923</th>\n",
              "      <td>GSE32154</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>S100P</td>\n",
              "      <td>Tretinoin</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tretinoin results in increased expression of S...</td>\n",
              "      <td>A24455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>924</th>\n",
              "      <td>GSE32154</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>S100P</td>\n",
              "      <td>Valproic acid</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Valproic Acid results in increased expression ...</td>\n",
              "      <td>A21035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>925</th>\n",
              "      <td>GSE32154</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>S100P</td>\n",
              "      <td>Zinc sulfate</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Zinc Sulfate results in increased expression o...</td>\n",
              "      <td>A24818</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>926 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       GSECODE  ...                                        REF\n",
              "0    GSE111906  ...                                        NaN\n",
              "1    GSE111906  ...                                        NaN\n",
              "2    GSE111906  ...                                        NaN\n",
              "3    GSE111906  ...  https://pubmed.ncbi.nlm.nih.gov/24690739/\n",
              "4    GSE111906  ...  https://pubmed.ncbi.nlm.nih.gov/24690739/\n",
              "..         ...  ...                                        ...\n",
              "921   GSE32154  ...                                     A24195\n",
              "922   GSE32154  ...                                     A22301\n",
              "923   GSE32154  ...                                     A24455\n",
              "924   GSE32154  ...                                     A21035\n",
              "925   GSE32154  ...                                     A24818\n",
              "\n",
              "[926 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 766
        },
        "id": "ZPRk8xEUPugj",
        "outputId": "475d048f-ee8d-4c99-bd0b-4731621061c8"
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "sns.set()\n",
        "plt.figure(figsize=(35, 12))\n",
        "ax = sns.countplot(x = 'GSECODE', data=dados_drugbank.query('GSECODE in @cod_gse'), order= dados_drugbank['GSECODE'].value_counts().index[0:10], palette='husl')\n",
        "ax.set_title('Top GSECODE')\n",
        "plt.title(\"Top GSECODE\", fontsize = 20)\n",
        "plt.xlabel(\"Top GSECODE\", fontsize = 18)\n",
        "plt.ylabel(\"count\", fontsize = 18)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 2520x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NCfDzlhRQnvr"
      },
      "source": [
        "cod_mps = dados_drugbank['MPS_TYPE'].value_counts().index[0:10]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oeSL5zp9QvbD",
        "outputId": "4a828e3b-a2b0-40f6-f670-b984360f1c45"
      },
      "source": [
        "cod_mps"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['MPSIIIB', 'MPSI'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "4m4EyAcfQvmh",
        "outputId": "9f2d89d1-334d-408a-ac8b-99af12e073c2"
      },
      "source": [
        "dados_drugbank.query('MPS_TYPE in @cod_mps')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GSECODE</th>\n",
              "      <th>MPS_TYPE</th>\n",
              "      <th>Symbol</th>\n",
              "      <th>DRUGBANK</th>\n",
              "      <th>EXPRESSION</th>\n",
              "      <th>MOA</th>\n",
              "      <th>REF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>EEF1A1P11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>RPL10P9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>ZBTB12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>MRPL45</td>\n",
              "      <td>Copper</td>\n",
              "      <td>DOWN</td>\n",
              "      <td>[Disulfiram binds to Copper] which results in ...</td>\n",
              "      <td>https://pubmed.ncbi.nlm.nih.gov/24690739/</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>MRPL45</td>\n",
              "      <td>Disulfiram</td>\n",
              "      <td>DOWN</td>\n",
              "      <td>[Disulfiram binds to Copper] which results in ...</td>\n",
              "      <td>https://pubmed.ncbi.nlm.nih.gov/24690739/</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>921</th>\n",
              "      <td>GSE32154</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>S100P</td>\n",
              "      <td>Silicon dioxide</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Silicon Dioxide analog results in decreased ex...</td>\n",
              "      <td>A24195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>922</th>\n",
              "      <td>GSE32154</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>S100P</td>\n",
              "      <td>Testosterone</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Testosterone results in increased expression o...</td>\n",
              "      <td>A22301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>923</th>\n",
              "      <td>GSE32154</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>S100P</td>\n",
              "      <td>Tretinoin</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tretinoin results in increased expression of S...</td>\n",
              "      <td>A24455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>924</th>\n",
              "      <td>GSE32154</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>S100P</td>\n",
              "      <td>Valproic acid</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Valproic Acid results in increased expression ...</td>\n",
              "      <td>A21035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>925</th>\n",
              "      <td>GSE32154</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>S100P</td>\n",
              "      <td>Zinc sulfate</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Zinc Sulfate results in increased expression o...</td>\n",
              "      <td>A24818</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>926 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       GSECODE  ...                                        REF\n",
              "0    GSE111906  ...                                        NaN\n",
              "1    GSE111906  ...                                        NaN\n",
              "2    GSE111906  ...                                        NaN\n",
              "3    GSE111906  ...  https://pubmed.ncbi.nlm.nih.gov/24690739/\n",
              "4    GSE111906  ...  https://pubmed.ncbi.nlm.nih.gov/24690739/\n",
              "..         ...  ...                                        ...\n",
              "921   GSE32154  ...                                     A24195\n",
              "922   GSE32154  ...                                     A22301\n",
              "923   GSE32154  ...                                     A24455\n",
              "924   GSE32154  ...                                     A21035\n",
              "925   GSE32154  ...                                     A24818\n",
              "\n",
              "[926 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 765
        },
        "id": "nmt5YLAlQv0G",
        "outputId": "37def96c-b84f-4d75-d66c-5ef9ff7e848a"
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "sns.set()\n",
        "plt.figure(figsize=(35, 12))\n",
        "ax = sns.countplot(x = 'MPS_TYPE', data=dados_drugbank.query('MPS_TYPE in @cod_mps'), order= dados_drugbank['MPS_TYPE'].value_counts().index[0:10], palette='husl')\n",
        "ax.set_title('Top MPS_TYPE')\n",
        "plt.title(\"Top MPS_TYPE\", fontsize = 20)\n",
        "plt.xlabel(\"Top MPS_TYPE\", fontsize = 18)\n",
        "plt.ylabel(\"count\", fontsize = 18)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 2520x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L0Hjqo1mRxsr"
      },
      "source": [
        "cod_moa = dados_drugbank['MOA'].value_counts().index[0:10]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CYk4LBH_Rx37",
        "outputId": "ca6f5c4e-67d6-4bd7-e80e-d8ef4417544b"
      },
      "source": [
        "cod_moa"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Valproic Acid results in increased expression of DNAJC8 mRNA',\n",
              "       '[Disulfiram binds to Copper] which results in decreased expression of DDIT3 mRNA',\n",
              "       '[Disulfiram binds to Copper] which results in increased expression of ASNS mRNA',\n",
              "       'Valproic Acid results in decreased expression of SOCS7 mRNA',\n",
              "       '[Disulfiram binds to Copper] which results in decreased expression of S100P mRNA',\n",
              "       '[Disulfiram binds to Copper] which results in decreased expression of MRPL45 mRNA',\n",
              "       'Cyclosporine results in increased expression of SOCS7 mRNA',\n",
              "       'Simvastatin results in increased expression of SOCS7 mRNA',\n",
              "       'Formaldehyde results in decreased expression of SOCS7 mRNA',\n",
              "       'Quercetin results in decreased expression of ZNRD1 mRNA'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "id": "JArhEdSWRyAm",
        "outputId": "78c2c545-59e7-4ca9-e297-baf66d9d564b"
      },
      "source": [
        "dados_drugbank.query('MOA in @cod_moa')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GSECODE</th>\n",
              "      <th>MPS_TYPE</th>\n",
              "      <th>Symbol</th>\n",
              "      <th>DRUGBANK</th>\n",
              "      <th>EXPRESSION</th>\n",
              "      <th>MOA</th>\n",
              "      <th>REF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>MRPL45</td>\n",
              "      <td>Copper</td>\n",
              "      <td>DOWN</td>\n",
              "      <td>[Disulfiram binds to Copper] which results in ...</td>\n",
              "      <td>https://pubmed.ncbi.nlm.nih.gov/24690739/</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>MRPL45</td>\n",
              "      <td>Disulfiram</td>\n",
              "      <td>DOWN</td>\n",
              "      <td>[Disulfiram binds to Copper] which results in ...</td>\n",
              "      <td>https://pubmed.ncbi.nlm.nih.gov/24690739/</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>ZNRD1</td>\n",
              "      <td>Quercetin</td>\n",
              "      <td>DOWN</td>\n",
              "      <td>Quercetin results in decreased expression of Z...</td>\n",
              "      <td>https://clincancerres.aacrjournals.org/content...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>SOCS7</td>\n",
              "      <td>Cyclosporine</td>\n",
              "      <td>UP</td>\n",
              "      <td>Cyclosporine results in increased expression o...</td>\n",
              "      <td>https://pubmed.ncbi.nlm.nih.gov/25562108/</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>SOCS7</td>\n",
              "      <td>Formaldehyde</td>\n",
              "      <td>DOWN</td>\n",
              "      <td>Formaldehyde results in decreased expression o...</td>\n",
              "      <td>https://pubmed.ncbi.nlm.nih.gov/20655997/</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>SOCS7</td>\n",
              "      <td>Simvastatin</td>\n",
              "      <td>UP</td>\n",
              "      <td>Simvastatin results in increased expression of...</td>\n",
              "      <td>https://pubmed.ncbi.nlm.nih.gov/18453621/</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>SOCS7</td>\n",
              "      <td>Valproic acid</td>\n",
              "      <td>DOWN</td>\n",
              "      <td>Valproic Acid results in decreased expression ...</td>\n",
              "      <td>https://pubmed.ncbi.nlm.nih.gov/23179753/</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>SOCS7</td>\n",
              "      <td>Cyclosporine</td>\n",
              "      <td>UP</td>\n",
              "      <td>Cyclosporine results in increased expression o...</td>\n",
              "      <td>https://pubmed.ncbi.nlm.nih.gov/25562108/</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>SOCS7</td>\n",
              "      <td>Formaldehyde</td>\n",
              "      <td>DOWN</td>\n",
              "      <td>Formaldehyde results in decreased expression o...</td>\n",
              "      <td>https://pubmed.ncbi.nlm.nih.gov/20655997/</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>SOCS7</td>\n",
              "      <td>Simvastatin</td>\n",
              "      <td>UP</td>\n",
              "      <td>Simvastatin results in increased expression of...</td>\n",
              "      <td>https://pubmed.ncbi.nlm.nih.gov/18453621/</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>GSE111906</td>\n",
              "      <td>MPSI</td>\n",
              "      <td>SOCS7</td>\n",
              "      <td>Valproic acid</td>\n",
              "      <td>DOWN</td>\n",
              "      <td>Valproic Acid results in decreased expression ...</td>\n",
              "      <td>https://pubmed.ncbi.nlm.nih.gov/27188386/</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>GSE23075</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>DDIT3</td>\n",
              "      <td>Copper</td>\n",
              "      <td>DOWN</td>\n",
              "      <td>[Disulfiram binds to Copper] which results in ...</td>\n",
              "      <td>A21750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>264</th>\n",
              "      <td>GSE23075</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>DDIT3</td>\n",
              "      <td>Disulfiram</td>\n",
              "      <td>DOWN</td>\n",
              "      <td>[Disulfiram binds to Copper] which results in ...</td>\n",
              "      <td>A21750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>609</th>\n",
              "      <td>GSE23075</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>ASNS</td>\n",
              "      <td>Copper</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[Disulfiram binds to Copper] which results in ...</td>\n",
              "      <td>A21750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>617</th>\n",
              "      <td>GSE23075</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>ASNS</td>\n",
              "      <td>Disulfiram</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[Disulfiram binds to Copper] which results in ...</td>\n",
              "      <td>A21750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>717</th>\n",
              "      <td>GSE32154</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>DNAJC8</td>\n",
              "      <td>Valproic acid</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Valproic Acid results in increased expression ...</td>\n",
              "      <td>A24690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>718</th>\n",
              "      <td>GSE32154</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>DUSP16</td>\n",
              "      <td>Valproic acid</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Valproic Acid results in increased expression ...</td>\n",
              "      <td>A24690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>900</th>\n",
              "      <td>GSE32154</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>S100P</td>\n",
              "      <td>Copper</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[Disulfiram binds to Copper] which results in ...</td>\n",
              "      <td>A21750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>909</th>\n",
              "      <td>GSE32154</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>S100P</td>\n",
              "      <td>Disulfiram</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[Disulfiram binds to Copper] which results in ...</td>\n",
              "      <td>A21750</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       GSECODE  ...                                                REF\n",
              "3    GSE111906  ...          https://pubmed.ncbi.nlm.nih.gov/24690739/\n",
              "4    GSE111906  ...          https://pubmed.ncbi.nlm.nih.gov/24690739/\n",
              "15   GSE111906  ...  https://clincancerres.aacrjournals.org/content...\n",
              "42   GSE111906  ...          https://pubmed.ncbi.nlm.nih.gov/25562108/\n",
              "43   GSE111906  ...          https://pubmed.ncbi.nlm.nih.gov/20655997/\n",
              "44   GSE111906  ...          https://pubmed.ncbi.nlm.nih.gov/18453621/\n",
              "45   GSE111906  ...          https://pubmed.ncbi.nlm.nih.gov/23179753/\n",
              "98   GSE111906  ...          https://pubmed.ncbi.nlm.nih.gov/25562108/\n",
              "99   GSE111906  ...          https://pubmed.ncbi.nlm.nih.gov/20655997/\n",
              "100  GSE111906  ...          https://pubmed.ncbi.nlm.nih.gov/18453621/\n",
              "101  GSE111906  ...          https://pubmed.ncbi.nlm.nih.gov/27188386/\n",
              "249   GSE23075  ...                                             A21750\n",
              "264   GSE23075  ...                                             A21750\n",
              "609   GSE23075  ...                                             A21750\n",
              "617   GSE23075  ...                                             A21750\n",
              "717   GSE32154  ...                                             A24690\n",
              "718   GSE32154  ...                                             A24690\n",
              "900   GSE32154  ...                                             A21750\n",
              "909   GSE32154  ...                                             A21750\n",
              "\n",
              "[19 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "mD8Zy6bRRyJd",
        "outputId": "309bafca-ce90-40bc-d40b-b847129efed0"
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "sns.set()\n",
        "plt.figure(figsize=(75, 20))\n",
        "ax = sns.countplot(x = 'MOA', data=dados_drugbank.query('MOA in @cod_moa'), order= dados_drugbank['MOA'].value_counts().index[0:10], palette='husl')\n",
        "ax.set_title('Top MOA')\n",
        "plt.title(\"Top MOA\", fontsize = 20)\n",
        "plt.xlabel(\"Top MOA\", fontsize = 18)\n",
        "plt.ylabel(\"count\", fontsize = 18)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 5400x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        },
        "id": "GsQFRA-KAV4x",
        "outputId": "900dd30d-e7ff-4411-d83f-af238b1fccce"
      },
      "source": [
        "dados_drugbank.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GSECODE</th>\n",
              "      <th>MPS TYPE</th>\n",
              "      <th>Symbol</th>\n",
              "      <th>DRUGBANK</th>\n",
              "      <th>EXPRESSION</th>\n",
              "      <th>MOA</th>\n",
              "      <th>REF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>926</td>\n",
              "      <td>926</td>\n",
              "      <td>926</td>\n",
              "      <td>900</td>\n",
              "      <td>568</td>\n",
              "      <td>900</td>\n",
              "      <td>887</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>120</td>\n",
              "      <td>194</td>\n",
              "      <td>2</td>\n",
              "      <td>891</td>\n",
              "      <td>383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>GSE23075</td>\n",
              "      <td>MPSIIIB</td>\n",
              "      <td>DDIT3</td>\n",
              "      <td>Valproic acid</td>\n",
              "      <td>UP</td>\n",
              "      <td>Simvastatin results in increased expression of...</td>\n",
              "      <td>A21035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>517</td>\n",
              "      <td>776</td>\n",
              "      <td>89</td>\n",
              "      <td>60</td>\n",
              "      <td>328</td>\n",
              "      <td>2</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         GSECODE  ...     REF\n",
              "count        926  ...     887\n",
              "unique         3  ...     383\n",
              "top     GSE23075  ...  A21035\n",
              "freq         517  ...      34\n",
              "\n",
              "[4 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    }
  ]
}