Gene Ontology and KEGG Pathway Enrichment Analysis of a Drug Target-Based Classification System.
Drug-target interaction (DTI) is a key aspect in pharmaceutical research. With the ever-increasing new drug data resources, computational approaches have emerged as powerful and labor-saving tools in predicting new DTIs. However, so far, most of these predictions have been based on structural simila...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2015-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4423955?pdf=render |
| _version_ | 1818275537795153920 |
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| author | Lei Chen Chen Chu Jing Lu Xiangyin Kong Tao Huang Yu-Dong Cai |
| author_facet | Lei Chen Chen Chu Jing Lu Xiangyin Kong Tao Huang Yu-Dong Cai |
| author_sort | Lei Chen |
| collection | DOAJ |
| description | Drug-target interaction (DTI) is a key aspect in pharmaceutical research. With the ever-increasing new drug data resources, computational approaches have emerged as powerful and labor-saving tools in predicting new DTIs. However, so far, most of these predictions have been based on structural similarities rather than biological relevance. In this study, we proposed for the first time a "GO and KEGG enrichment score" method to represent a certain category of drug molecules by further classification and interpretation of the DTI database. A benchmark dataset consisting of 2,015 drugs that are assigned to nine categories ((1) G protein-coupled receptors, (2) cytokine receptors, (3) nuclear receptors, (4) ion channels, (5) transporters, (6) enzymes, (7) protein kinases, (8) cellular antigens and (9) pathogens) was constructed by collecting data from KEGG. We analyzed each category and each drug for its contribution in GO terms and KEGG pathways using the popular feature selection "minimum redundancy maximum relevance (mRMR)" method, and key GO terms and KEGG pathways were extracted. Our analysis revealed the top enriched GO terms and KEGG pathways of each drug category, which were highly enriched in the literature and clinical trials. Our results provide for the first time the biological relevance among drugs, targets and biological functions, which serves as a new basis for future DTI predictions. |
| first_indexed | 2024-12-12T22:31:20Z |
| format | Article |
| id | doaj.art-2447f6473e76479eac3fa21e6310b4ce |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-12T22:31:20Z |
| publishDate | 2015-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-2447f6473e76479eac3fa21e6310b4ce2022-12-22T00:09:36ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-01105e012649210.1371/journal.pone.0126492Gene Ontology and KEGG Pathway Enrichment Analysis of a Drug Target-Based Classification System.Lei ChenChen ChuJing LuXiangyin KongTao HuangYu-Dong CaiDrug-target interaction (DTI) is a key aspect in pharmaceutical research. With the ever-increasing new drug data resources, computational approaches have emerged as powerful and labor-saving tools in predicting new DTIs. However, so far, most of these predictions have been based on structural similarities rather than biological relevance. In this study, we proposed for the first time a "GO and KEGG enrichment score" method to represent a certain category of drug molecules by further classification and interpretation of the DTI database. A benchmark dataset consisting of 2,015 drugs that are assigned to nine categories ((1) G protein-coupled receptors, (2) cytokine receptors, (3) nuclear receptors, (4) ion channels, (5) transporters, (6) enzymes, (7) protein kinases, (8) cellular antigens and (9) pathogens) was constructed by collecting data from KEGG. We analyzed each category and each drug for its contribution in GO terms and KEGG pathways using the popular feature selection "minimum redundancy maximum relevance (mRMR)" method, and key GO terms and KEGG pathways were extracted. Our analysis revealed the top enriched GO terms and KEGG pathways of each drug category, which were highly enriched in the literature and clinical trials. Our results provide for the first time the biological relevance among drugs, targets and biological functions, which serves as a new basis for future DTI predictions.http://europepmc.org/articles/PMC4423955?pdf=render |
| spellingShingle | Lei Chen Chen Chu Jing Lu Xiangyin Kong Tao Huang Yu-Dong Cai Gene Ontology and KEGG Pathway Enrichment Analysis of a Drug Target-Based Classification System. PLoS ONE |
| title | Gene Ontology and KEGG Pathway Enrichment Analysis of a Drug Target-Based Classification System. |
| title_full | Gene Ontology and KEGG Pathway Enrichment Analysis of a Drug Target-Based Classification System. |
| title_fullStr | Gene Ontology and KEGG Pathway Enrichment Analysis of a Drug Target-Based Classification System. |
| title_full_unstemmed | Gene Ontology and KEGG Pathway Enrichment Analysis of a Drug Target-Based Classification System. |
| title_short | Gene Ontology and KEGG Pathway Enrichment Analysis of a Drug Target-Based Classification System. |
| title_sort | gene ontology and kegg pathway enrichment analysis of a drug target based classification system |
| url | http://europepmc.org/articles/PMC4423955?pdf=render |
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