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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4423955/ https://www.ncbi.nlm.nih.gov/pubmed/25951454 http://dx.doi.org/10.1371/journal.pone.0126492 |
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author | Chen, Lei Chu, Chen Lu, Jing Kong, Xiangyin Huang, Tao Cai, Yu-Dong |
author_facet | Chen, Lei Chu, Chen Lu, Jing Kong, Xiangyin Huang, Tao Cai, Yu-Dong |
author_sort | Chen, Lei |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-4423955 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-44239552015-05-13 Gene Ontology and KEGG Pathway Enrichment Analysis of a Drug Target-Based Classification System Chen, Lei Chu, Chen Lu, Jing Kong, Xiangyin Huang, Tao Cai, Yu-Dong PLoS One Research Article 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. Public Library of Science 2015-05-07 /pmc/articles/PMC4423955/ /pubmed/25951454 http://dx.doi.org/10.1371/journal.pone.0126492 Text en © 2015 Chen et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Chen, Lei Chu, Chen Lu, Jing Kong, Xiangyin Huang, Tao Cai, Yu-Dong Gene Ontology and KEGG Pathway Enrichment Analysis of a Drug Target-Based Classification System |
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 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4423955/ https://www.ncbi.nlm.nih.gov/pubmed/25951454 http://dx.doi.org/10.1371/journal.pone.0126492 |
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