title: Gene Ontology and KEGG Pathway Enrichment Analysis of a Drug Target-Based Classification System.
abstract: 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.
authors.affiliation: College of Life Science, Shanghai University, Shanghai, People's Republic of China; College of Information Engineering, Shanghai Maritime University, Shanghai, People's Republic of China.
authors.forename: Lei
authors.initials: L
authors.lastname: Chen
authors.affiliation: Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China.
authors.forename: Chen
authors.initials: C
authors.lastname: Chu
authors.affiliation: Department of Medicinal Chemistry, School of Pharmacy, Yantai University, Shandong, Yantai, People's Republic of China.
authors.forename: Jing
authors.initials: J
authors.lastname: Lu
authors.affiliation: Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China.
authors.forename: Xiangyin
authors.initials: X
authors.lastname: Kong
authors.affiliation: Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China.
authors.forename: Tao
authors.initials: T
authors.lastname: Huang
authors.affiliation: College of Life Science, Shanghai University, Shanghai, People's Republic of China.
authors.forename: Yu-Dong
authors.initials: YD
authors.lastname: Cai
mesh_terms: D030541:Databases, Genetic; D004347:Drug Interactions; D063990:Gene Ontology