#Table of Contents

• Introduction
• Data
  • TCDB database
• Preprocessing
• Feature generation
  • BLAST features
  • Interproscan features
  • Data statistics of BLAST and IPS featuers
  • PSSM features
    • PSSM features generated from CDD database
    • PSSM features generated from TCDB
  • PSSM features generated from TCDB-CDD
  • Statistics of feature sets
• Empirical evaluation
  • Support vector machines (SVM)
    • SVM experiment settings
    • SVM results
  • Multiple kernel learning (MKL)
    • MKL experiment settings
    • MKL kernel weights
    • Support vector machine (SVM)
    • Max margin conditional random field (MMCRF)
    • Max margin regression (MMR)
• Future work

Introduction

The final goal of this project is to reliably predict the protein functions in terms of transporter function classification. The fucntion is defined as a TC code following the hierarchical structure (a tree) of the transporter protein classification system. In particular, there are five levels in the hierarchical classification system of which we aim to prediction the first four levels. This is due to the fact that the functional annotations on the fifth level are very specific.

We collection over 12,000 protein sequences from TCDB database and generate for each sequence a varity of features of the following three main categories

1. BLAST feature.
2. InterProScan features.
3. Position specific scoring matrix features.

In the end, we are able to have a collection of about 30 different feature sets. For each feature set we run single label classification model to predict functions of proteins. In addition, we combine different types of features with three multiple kernel learning approaches to build a better feature representation of the protein space, and improve prediction performance with the combine feature space. Furthermore, we adpot structured output learning approach to further improve the performance taking into consideration the correlation of different annotations given by the hierarchical structure.

Data

TCDB database

We extract protein classification data from TCDB database. As the intersection of the protein data listed above and the ones in TCDB is very small we compute protein features via BLAST and InterProScan.

1. Transport protein classification data are downloaded from TCDB database. NOTE: Su, her you should specify which version has been considered (I guess a version after 7 june)

   Type of data	Number of items
   Protein	12515
   TCDB annotation	9456

   1. The annotation of TCDB follows a five level classification hierarchy. The number of classes in each level is shown in the following table

      Level	Number of classes
      1	7
      2	30
      3	868
      4	2240
      5	9483

2. Data file for TCDB sequence and classification information is in the file ./Data/tcdb.1.

Preprocessing

1. First I remove duplicated proteins from the transporter protein classification database (TCDB), particularly, by analyzing the sequence-classification data file.

2. Then I run BLAST search and InterProScan for all preprocessed proteins.

3. Merge TCDB classification (protein labels), TCDB BLAST features, and TCDB InterProScan features.

4. Make data matrices of different types, e.g., different feature matrices and label matrix.

   1. In particular, BLAST features use real number (BLAST score), other InterProScan feature use integer number as count

5. Important scripts and result files are listed as follows.

   Preprocessing
   |---Bins
       |---process_tcdb.py        # process original TCDB database (remove duplication ect)
       |---merge_tcdb_blast_and_ips.py          # merge TCDB blast, ips and classfiication data
       |---run_blast.sh           # run _BLAST_ search
       |---run_interproscan.sh    # run interproscan search to generate protein features
       |---separate_different_features.py # generate feature matrices of different types
   |---Results
       |---tcdbdata               # merged data in sparse matrix format: 'protein name' 'feature name' 'value' 
       |---tcdbdata.collab        # feature names
       |---tcdbdata.rowlab        # protein names
       |---tcdbdata.mtx           # sparse data matrix with format 'protein id' 'feature id' 'value'
    	  |---tcdb1.annotations.gz   # dense matrix (text file gzipped) with Swissprot AC on rows and TCDB classes on columns. 
    	                             # Entry (i,j) of the matrix is 1 if protein i is annotated to TC class j, otherwise (i,j) = 0
    								 # this matrix includes all the annotations (including multiple paths) obtained from the file tcdb.1 in the Data directory.
   Experiments
   |---Data
       |---tcdb.prefix       # data matrices of different type, where type information are explained in the section of Data statistics.
       |---README.md         # read me file for experimental data
       |---tcdb.collab       # feature names
       |---tcdb.rowlab       # protein names
   |---Data.tar.gz           # compressed Data files, including files in `./Data` folder
   

Feature generation

BLAST features

1. BLAST with TCDB

   1. Protein sequences are aligned with themselves by running BLAST algorithms.

   2. This procedure will genrate a pairwise similarity matrix.

   3. Instruction for installing and running BLAST can be found from my blog post.

   4. In particular, after removing some replicated proteins, there are 12515 protein left in TCDB which will be used to build a TCDB BLAST database.

   5. The cleaned TCDB data file is located in ./Data/tcdb.

   6. For the BLAST search, we obtain all hits with e-value below 0.01.

   7. We use BLAST score as similary measure between pair of proteins.

   8. Some statistics about the TCDB BLAST features are shown in the following table

      Type of Data	Number of items
      Protein	12515
      TCDB BLAST feature	12515

   9. Data file for TCDB BLAST feature is located as ./Data/tcdbblast.

      1. The format of this file is

         qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore.

      2. The keywords are explained in the following table

         Keyword	Representation
         qseqid	Query Seq-id
         sseqid	Subject Seq-id
         sallacc	All subject accessions
         qstart	Start of alignment in query
         qend	End of alignment in query
         sstart	Start of alignment in subject
         send	End of alignment in subject
         evalue	Expect value
         bitscore	Bit score
         score	Raw score
         length	Alignment length
         pident	Percentage of identical matches
         mismatch	Number of mismatches
         gapopen	Number of gap openings

Interproscan features

1. InterProScan

   1. For all proteins in TCDB, we extract various protein features by running InterProScan.

   2. As this procedure takes time, we would like to install and run the InterProScan on local machines other than on webservers.

   3. A brief instruction for installation and running InterProScan is documented in my blog post.

   4. In particular, we download the InterProScan package together with various databases. Version information of InterProScan software and databases can be found from the following table

      Tool	Version	Information
      InterProScan	5.13-52.0	InterProScan package
      ProDom	2006.1	ProDom is a comprehensive set of protein domain families automatically generated from the UniProt Knowledge Database.
      HAMAP		High-quality Automated and Manual Annotation of Microbial Proteomes
      SMART	6.2	SMART allows the identification and analysis of domain architectures based on Hidden Markov Models or HMMs
      SuperFamily	1.75	SUPERFAMILY is a database of structural and functional annotation for all proteins and genomes.
      PRINTS	42.0	A fingerprint is a group of conserved motifs used to characterise a protein family
      Panther	9.0	The PANTHER (Protein ANalysis THrough Evolutionary Relationships) Classification System is a unique resource that classifies genes by their functions, using published scientific experimental evidence and evolutionary relationships to predict function even in the absence of direct experimental evidence.
      Gene3d	3.5.0	Structural assignment for whole genes and genomes using the CATH domain structure database
      PIRSF	3.01	The PIRSF concept is being used as a guiding principle to provide comprehensive and non-overlapping clustering of UniProtKB sequences into a hierarchical order to reflect their evolutionary relationships.
      PfamA	27.0	A large collection of protein families, each represented by multiple sequence alignments and hidden Markov models (HMMs)
      PrositeProfiles		PROSITE consists of documentation entries describing protein domains, families and functional sites as well as associated patterns and profiles to identify them
      TIGRFAM	15.0	TIGRFAMs are protein families based on Hidden Markov Models or HMMs
      PrositePatterns		PROSITE consists of documentation entries describing protein domains, families and functional sites as well as associated patterns and profiles to identify them
      Coils	2.2	Prediction of Coiled Coil Regions in Proteins
      TMHMM	2.0	Prediction of transmembrane helices in proteins
      Phobius	1.01	A combined transmembrane topology and signal peptide predictor
      SignalP GRAM NEGATIVE	4.0	SignalP (organism type gram-negative prokaryotes) predicts the presence and location of signal peptide cleavage sites in amino acid sequences for gram-negative prokaryotes
      SignalP EUK	4.0	SignalP (organism type eukaryotes) predicts the presence and location of signal peptide cleavage sites in amino acid sequences for eukaryotes.
      SignalP GRAM POSITIVE	4.0	SignalP (organism type gram-positive prokaryotes) predicts the presence and location of signal peptide cleavage sites in amino acid sequences for gram-positive prokaryotes

   5. Note that the last five tools and Panthon database are installed into InterProScan manually.

   6. It is not necessary to perform again the scanning with InterProScan for all TCDB sequences as most of the TCDB sequences already have UniProt accession number. Therefore, we depend on the lookup service provided by InterProScan in order to directly extract the sequence features from the database.

   7. In addition to direct extraction, protein sequences that is not known to InterProScan are scanned.

   8. Interproscan results is located as ./Data/tcdbips. The file follows the structure described in the following table

      Column	Description
      1	Protein Accession (e.g. P51587)
      2	Sequence MD5 digest (e.g. 14086411a2cdf1c4cba63020e1622579)
      3	Sequence Length (e.g. 3418)
      4	Analysis (e.g. Pfam / PRINTS / Gene3D)
      5	Signature Accession (e.g. PF09103 / G3DSA:2.40.50.140)
      6	Signature Description (e.g. BRCA2 repeat profile)
      7	Start location
      8	Stop location
      9	Score - is the e-value of the match reported by member database method (e.g. 3.1E-52)
      10	Status - is the status of the match (T: true)
      11	Date - is the date of the run
      12	InterPro annotations - accession (e.g. IPR002093)
      13	InterPro annotations - description (e.g. BRCA2 repeat)
      14	GO annotations (e.g. GO:0005515)
      15	Pathways annotations (e.g. REACT_71)

2. Besides the data files described above, all original data are located in the directory ./Data/

Data statistics of BLAST and IPS featuers

1. I compute the following statistice for the overall merged dataset, in particular, for the file ./Preprocessing/Results/tcdb.mtx.

   Type of statistics	Value
   Number of proteins	12546
   Number of features	25704
   Number of categories	20

2. Category information are listed in the table as follows:

   Prefix	Size	Feature	Version	Feature description
   TC__	3145	TCDB		TCDB classification
   TB__	12535	BLAST		BLAST search
   TIProDom__	145	ProDom	2006.1	ProDom is a comprehensive set of protein domain families automatically generated from the UniProt Knowledge Database.
   TIHamap__	209	HAMAP		High-quality Automated and Manual Annotation of Microbial Proteomes
   TISMART__	240	SMART	6.2	SMART allows the identification and analysis of domain architectures based on Hidden Markov Models or HMMs
   TISUPERFAMILY__	512	SuperFamily	1.75	SUPERFAMILY is a database of structural and functional annotation for all proteins and genomes.
   TIPRINTS__	579	PRINTS	42.0	A fingerprint is a group of conserved motifs used to characterise a protein family
   TIPANTHER__	4070	Panther	9.0	The PANTHER (Protein ANalysis THrough Evolutionary Relationships) Classification System is a unique resource that classifies genes by their functions, using published scientific experimental evidence and evolutionary relationships to predict function even in the absence of direct experimental evidence.
   TIGene3D__	611	Gene3d	3.5.0	Structural assignment for whole genes and genomes using the CATH domain structure database
   TIPIRSF__	283	PIRSF	3.01	The PIRSF concept is being used as a guiding principle to provide comprehensive and non-overlapping clustering of UniProtKB sequences into a hierarchical order to reflect their evolutionary relationships.
   TIPfam__	2025	PfamA	27.0	A large collection of protein families, each represented by multiple sequence alignments and hidden Markov models (HMMs)
   TIProSiteProfiles__	282	PrositeProfiles		PROSITE consists of documentation entries describing protein domains, families and functional sites as well as associated patterns and profiles to identify them
   TITIGRFAM__	769	TIGRFAM	15.0	TIGRFAMs are protein families based on Hidden Markov Models or HMMs
   TIProSitePatterns__	285	PrositePatterns		PROSITE consists of documentation entries describing protein domains, families and functional sites as well as associated patterns and profiles to identify them
   TICoils__	1	Coils	2.2	Prediction of Coiled Coil Regions in Proteins
   TITMHMM__	1	TMHMM	2.0	Prediction of transmembrane helices in proteins
   TIPhobius__	7	Phobius	1.01	A combined transmembrane topology and signal peptide predictor
   TISignalP_ GRAM_NEGATIVE__	2	SignalP GRAM NEGATIVE	4.0	SignalP (organism type gram-negative prokaryotes) predicts the presence and location of signal peptide cleavage sites in amino acid sequences for gram-negative prokaryotes
   TISignalP_ EUK__	2	SignalP EyUK	4.0	SignalP (organism type eukaryotes) predicts the presence and location of signal peptide cleavage sites in amino acid sequences for eukaryotes.
   TISignalP_ GRAM_POSITIVE__	1	SignalP GRAM POSITIVE	4.0	SignalP (organism type gram-positive prokaryotes) predicts the presence and location of signal peptide cleavage sites in amino acid sequences for gram-positive prokaryotes

PSSM features

PSSM features generated from CDD database

1. There are many public databases which contains conserved protein domain information in position specific scoring matrix PSSM format.

2. This section describes how to compute position specific scoring matrix PSSM features from public conserved domain databases CDD.

3. Download CDD database source files from NCBI FTP server.

4. Download CDD database version file from NCBI FTP server.

5. Download PSSM version file from NCBI FTP server.

6. The following table shows the statistics and version information of all databases in CDD.

   Database	Number of PSSM models	Version
   CDD	47363	v3.14
   Pfam	14831	v27.0
   COG	4825	v1.0
   KOG	4875	v1.0
   SMART	1013	v6.0
   PRK	10885	v6.9
   TiGRFAM	4488	v15.0
   CDD NCBI	11273

7. Build local CDD databases from different source files using NCBI Blast+ tool according to the following commands.

   ../makeprofiledb -title SMART -in Smart.pn -out Smart -threshold 9.82 -scale 100.0 -dbtype rps -index true

   ../makeprofiledb -title Pfam -in Pfam.pn -out Pfam -threshold 9.82 -scale 100.0 -dbtype rps -index true

   ../makeprofiledb -title COG -in Cog.pn -out Cog -threshold 9.82 -scale 100.0 -dbtype rps -index true

   ../makeprofiledb -title KOG -in Kog.pn -out Kog -threshold 9.82 -scale 100.0 -dbtype rps -index true

   ../makeprofiledb -title CDD_NCBI -in Cdd_NCBI.pn -out Cdd_NCBI -threshold 9.82 -scale 100.0 -dbtype rps -index true

   ../makeprofiledb -title PRK -in Prk.pn -out Prk -threshold 9.82 -scale 100.0 -dbtype rps -index true

   ../makeprofiledb -title Tigr -in Tigr.pn -out Tigr -threshold 9.82 -scale 100.0 -dbtype rps -index true

8. A CDD database covering all sources can be built with the following command

   ../makeprofiledb -title CDD -in Cdd.pn -out Cdd -threshold 9.82 -scale 100.0 -dbtype rps -index true

9. Once the databases are ready, BLAST search can be performed with rpsblast with the following command (e.g., search against SMART CDD).

   ../Blast/ncbi-blast-2.2.31+/bin/rpsblast -evalue 0.01 -num_threads 4 -outfmt "6 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore" -db ../Blast/ncbi-blast-2.2.31+/bin/CDD/SMART -query ../../Data/tcdb -out ../../Data/tcdbrpsSMART

10. The complete code is available from my GitHub.

PSSM features generated from TCDB

1. psiblast provides an alternative to search against TCDB database with PSSM features.

2. In particular, each protein sequence is searched against TCDB and a PSSM is computed for this sequence.

3. After that, the system use constructed PSSM to perform a second search against TCDB.

4. The command to perform psiblast search is shown as the following.

   ../../makeprofiledb -title TCDB201509PSSM -in tcdb201509pssm.pn -out tcdb201509pssm -threshold 9.82 -scale 100.0 -dbtype rps -index true

5. The complete code is available from my GitHub.

PSSM features generated from TCDB-CDD

1. As an alterntive, one can search each protein sequence against TCDB and generate a PSSM pattern.

2. With all generate PSSM patterns, one can build a CDD database of TCDB protein sequences.

3. After that, each protein sequence is search against the CDD dataabse of TCDB protein sequences.

4. The complete code is available from my GitHub.

5. Statistics of CDD database of TCDB sequences is shown in the following table.

   Database	Number of PSSM models	Version
   CDD-TCDB	12540	201509 version of TCDB

Statistics of feature sets

The following table shows the statistics of features computed for TCDB prediction tasks.

Feature set	Version	Number of features
BLAST-TCDB	v201506	12535
IPS-ProDom	v200601	145
IPS-HAMAP	v1.0	209
IPS-SMART	v6.2	240
IPS-SuperFamily	v1.75	512
IPS-PRINTS	v42.0	579
IPS-Panther	v9.0	4070
IPS-Gene3d	v3.5.0	611
IPS-PIRSF	v3.01	283
IPS-PfamA	v27.0	2025
IPS-PrositeProfiles	v1.0	282
IPS-TIGRFAM	v15.0	769
IPS-PrositePatterns	v1.0	285
IPS-Coils	v2.2	1
IPS-TMHMM	v2.0	1
IPS-Phobius	v1.01	7
IPS-SignalP GRAM NEGATIVE	v4.0	2
IPS-SignalP EyUK	v4.0	2
IPS-SignalP GRAM POSITIVE	v4.0	1
PSSM-CDD	v3.14	47363
PSSM-Pfam	v27.0	14831
PSSM-COG	v1.0	4825
PSSM-KOG	v1.0	4875
PSSM-SMART	v6.0	1013
PSSM-PRK	v6.9	10885
PSSM-TiGRFAM	v15.0	4488
PSSM-CDDNCBI	v201506	11273
PSSM-PSITCDB	v201506	12540
PSSM-RPSTCDB	v201506	12531

Empirical evaluation

Support vector machines (SVM)

In this section, we test the classification performance on transporter classification (TC) based on different input feature maps as discussed in the previous section.

SVM experiment settings

1. Linear SVM is used as the baseline learner. We use a SVM implementation from libSVM.
2. We select for each input feature map a SVM margin slack (C) parameter from the set {0.01,0.1,1,10,100}.
3. Parameter selection is baed on a random selection of 5000 proteins from the original dataset.
4. After parametere selection, the best SVM C parameter is applied to both training and test.
5. Experiment results are reported from a five fold cross validation procedure. We randomly divide examples into five disjoin set of equal size. In each iteration, we use one set for testing and the rest for training. The same procedure is then repeated five times.
6. We report the following metrics to measure the performance of the classifier including AUC, accuracy, F1, precision, and recall. The scores are computed by pooling all microlabels.

SVM results

1. Preliminary experimental results are shown in the following table

   Input feature	AUC	Microlabel Accuracy	F1	Precision	Recall	Multilabel Accuracy
   TB	0.8705	0.9980	0.0023	1.0000	0.0012	0.0000
   TICoils	0.8705	0.9980	0.0023	1.0000	0.0012	0.0000
   TIGene3D	0.8729	0.9982	0.2151	0.9255	0.1217	0.0046
   TIHamap	0.8714	0.9980	0.0122	0.8896	0.0061	0.0000
   TIPANTHER	0.8721	0.9981	0.0335	0.9857	0.0170	0.0000
   TIPfam	0.8727	0.9981	0.0985	0.6216	0.0535	0.0000
   TIPhobius	0.9048	0.9979	0.2111	0.3945	0.1441	0.0006
   TIPIRSF	0.8714	0.9980	0.0208	0.9941	0.0105	0.0004
   TIPRINTS	0.8730	0.9981	0.0630	0.9765	0.0325	0.0074
   TIProDom	0.8711	0.9980	0.0036	0.9886	0.0018	0.0000
   TIProSitePatterns	0.8720	0.9981	0.1007	0.9896	0.0531	0.0039
   TIProSiteProfiles	0.8727	0.9982	0.1976	0.9925	0.1097	0.0041
   TISignalP_EUK	0.8714	0.9980	0.0481	0.5812	0.0251	0.0000
   TISignalP_GRAM_NEGATIVE	0.8708	0.9980	0.0377	0.6214	0.0194	0.0000
   TISignalP_GRAM_POSITIVE	0.8722	0.9980	0.0413	0.5434	0.0214	0.0000
   TISMART	0.8734	0.9981	0.0933	0.9577	0.0491	0.0015
   TISUPERFAMILY	0.8810	0.9983	0.3264	0.8470	0.2022	0.0076
   TITIGRFAM	0.8721	0.9981	0.0401	0.9861	0.0205	0.0002
   TITMHMM	0.8706	0.9980	0.1953	0.5192	0.1203	0.0009
   TPSI	0.8705	0.9980	0.0082	0.9804	0.0041	0.0000
   TRPSCDD	0.8705	0.9980	0.0023	1.0000	0.0012	0.0000
   TRPSCDDNCBI	0.8710	0.9981	0.0952	0.9635	0.0501	0.0015
   TRPSCOG	0.8860	0.9982	0.2196	0.9124	0.1248	0.0091
   TRPSKOG	0.8808	0.9982	0.1956	0.9352	0.1092	0.0142
   TRPSPFAM	0.8715	0.9980	0.0252	0.9968	0.0128	0.0000
   TRPSPRK	0.8717	0.9981	0.1247	0.7926	0.0677	0.0019
   TRPSSMART	0.8712	0.9981	0.0890	0.9457	0.0467	0.0014
   TRPSTCDB201509PSSM	0.8705	0.9980	0.0023	1.0000	0.0012	0.0000
   TRPSTIGR	0.8728	0.9981	0.1134	0.9888	0.0601	0.0037

Multiple kernel learning (MKL)

In stead of predicting the transporter classification (TC) with single feature map as studied in the previous section, we aim to combine those 19 different feature maps with multiple kernel learning approaches.

MKL experiment settings

1. We compute an input base kernel (gram matrix) for each individual feature map. In particular, each base kenrel is a linear kernel on the original feature map.
2. We compute a linear output kernel for the output multilabels.
3. Three multiple kernel learning approaches are applied to combine these 19 base kernels including
   1. UNIMKL which computes a unifom combination of based kenrels
   2. ALIGN which aligns each input kernel with the output kernel, then combines all based kernels according to the alignment scores
   3. ALIGNF which maximizes the alignment score between the output kernel and a convex combination of all input kernels
4. Support Vector Machines are used to build the classification model. In particular, we used a LibSVM version with precomputed kernel.
5. We select SVM margin slack parameter (C) based on a random selection of 5000 examples. Best C is selected from the set {0.01,0.1,1,10,100}.
6. After parameter selection, best C parameter is used for training and prediction.
7. Experiment results are reported from a five fold cross validation procedure. We randomly divide examples into five disjoin set of equal size. In each iteration, we use one set for testing and the rest for training. The same procedure is then repeated five times.
8. We report the following metrics to measure the performance of the classifier including AUC (area under the curve), AUPRC (area under the precision-recall curve), accuracy, F1, precision, and recall. The scores are computed by pooling all microlabels. In addition, precision, recall, and accuracy are computed with a threshold 0.5 to binarize all real valued predictions.

MKL kernel weights

1. Kernel weights computed from different multiple kernel learning approaches are listed in the following table

Base kernel	UNIMKL	ALIGN	ALIGNF
TB.K	0.03	0.17	0.00
TICoils.K	0.03	0.01	0.02
TIGene3D.K	0.03	0.14	0.00
TIHamap.K	0.03	0.02	0.29
TIPANTHER.K	0.03	0.14	0.08
TIPfam.K	0.03	0.19	0.29
TIPhobius.K	0.03	0.20	0.22
TIPIRSF.K	0.03	0.02	0.11
TIPRINTS.K	0.03	0.02	0.01
TIProDom.K	0.03	0.01	0.14
TIProSitePatterns.K	0.03	0.09	0.00
TIProSiteProfiles.K	0.03	0.14	0.01
TISignalP_EUK.K	0.03	0.06	0.03
TISignalP_GRAM_NEGATIVE.K	0.03	0.06	0.08
TISignalP_GRAM_POSITIVE.K	0.03	0.05	0.00
TISMART.K	0.03	0.14	0.00
TISUPERFAMILY.K	0.03	0.16	0.11
TITIGRFAM.K	0.03	0.04	0.06
TITMHMM.K	0.03	0.18	0.00
TPSI.K	0.03	0.24	0.20
TRPSCDD.K	0.03	0.03	0.00
TRPSCDDNCBI.K	0.03	0.21	0.24
TRPSCOG.K	0.03	0.24	0.64
TRPSKOG.K	0.03	0.11	0.00
TRPSPFAM.K	0.03	0.21	0.33
TRPSPRK.K	0.03	0.19	0.01
TRPSSMART.K	0.03	0.14	0.08
TRPSTCDB201509PSSM.K	0.03	0.25	0.31
TRPSTIGR.K	0.03	0.20	0.00

1. In addition, kernel weights are shown in the following bar plot

   

Support vector machine (SVM)

Prediction performances of three multiple kernel learning approaches are listed in the following table in which all combined kernel are centered->normalized->centered. In addition, we use Guassian kenrel on all three computed kernels, the corresponding prediction performance is shown in the following table.

MKL	Kernel	AUC	Microlabel Accuracy	F1	Precision	Recall	Multilabel Accuracy
Linear	UNIMKL	0.9942	0.9995	0.8485	0.9344	0.7770	0.5327
Linear	ALIGN	0.9946	0.9995	0.8621	0.9377	0.7978	0.5484
Linear	ALIGNF	0.9928	0.9996	0.8822	0.9485	0.8245	0.6048
Gaussian	UNIMKL	0.9303	0.9988	0.5568	0.9619	0.3918	0.2315
Gaussian	ALIGN	0.9169	0.9985	0.3938	0.9722	0.2469	0.1595
Gaussian	ALIGNF	0.9461	0.9990	0.6609	0.9619	0.5034	0.3289

Max margin conditional random field (MMCRF)

Prediction performance of the developed structured output prediction method is shown in the following table. In particular, kernels are computed from multiple kernel learning approaches. Predidction performance of the developed structured output prediction model with additional Gaussian kernels on kernel matrices that are computed from multiple kernel learning approaches.

MMCRF	Kernel	AUC	Microlabel Accuracy	F1	Precision	Recall	Multilabel Accuracy
Linear	UNIMKL	NA	0.9995	0.7957	0.7957	0.7957	0.6176
Linear	ALIGN	NA	0.9995	0.8174	0.8174	0.8174	0.6334
Linear	ALIGNF	NA	0.9996	0.8240	0.8240	0.8240	0.6426
Gaussian	UNIMKL	NA	0.9996	0.8369	0.8369	0.8369	0.6977
Gaussian	ALIGN	NA	0.9996	0.8615	0.8615	0.8615	0.7421
Gaussian	ALIGNF	NA	0.9996	0.8537	0.8537	0.8537	0.7118

Max margin regression (MMR)

Prediction performance of MMR is shown in the following table. In particular, kernels are computed directly from multiple kernel learning approaches. Predidction performance of MMR with additional Gaussian kernels on kernel matrices that are computed from multiple kernel learning approaches.

MMR	Kernel	AUC	Microlabel Accuracy	F1	Precision	Recall	Multilabel Accuracy
Linear	UNIMKL	NA	NA	0.4332	0.4332	0.4332	0.1198
Linear	ALIGN	NA	NA	0.4384	0.4384	0.4384	0.1241
Linear	ALIGNF	NA	NA	0.4955	0.4955	0.4955	0.1752
Gaussian	UNIMKL	NA	NA	0.8354	0.8354	0.8354	0.6811
Gaussian	ALIGN	NA	NA	0.8550	0.8550	0.8550	0.7191
Gaussian	ALIGNF	NA	NA	0.8463	0.8463	0.8463	0.6839

Future work