title: Analysis and prediction of drug-drug interaction by minimum redundancy maximum relevance and incremental feature selection.
abstract: Drug-drug interaction (DDI) defines a situation in which one drug affects the activity of another when both are administered together. DDI is a common cause of adverse drug reactions and sometimes also leads to improved therapeutic effects. Therefore, it is of great interest to discover novel DDIs according to their molecular properties and mechanisms in a robust and rigorous way. This paper attempts to predict effective DDIs using the following properties: (1) chemical interaction between drugs; (2) protein interactions between the targets of drugs; and (3) target enrichment of KEGG pathways. The data consisted of 7323 pairs of DDIs collected from the DrugBank and 36,615 pairs of drugs constructed by randomly combining two drugs. Each drug pair was represented by 465 features derived from the aforementioned three categories of properties. The random forest algorithm was adopted to train the prediction model. Some feature selection techniques, including minimum redundancy maximum relevance and incremental feature selection, were used to extract key features as the optimal input for the prediction model. The extracted key features may help to gain insights into the mechanisms of DDIs and provide some guidelines for the relevant clinical medication developments, and the prediction model can give new clues for identification of novel DDIs.
authors.affiliation: a Intelligence Research Department, Information Center , Shanghai Institute of Materia Medica, Chinese Academy of Sciences , Shanghai 201203 , P. R. China.
authors.forename: Lili
authors.initials: L
authors.lastname: Liu
authors.affiliation: b College of Information Engineering, Shanghai Maritime University , Shanghai 201306 , P. R. China.
authors.forename: Lei
authors.initials: L
authors.lastname: Chen
authors.affiliation: c Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences , Shanghai 200031 , P. R. China.
authors.forename: Yu-Hang
authors.initials: YH
authors.lastname: Zhang
authors.affiliation: b College of Information Engineering, Shanghai Maritime University , Shanghai 201306 , P. R. China.
authors.forename: Lai
authors.initials: L
authors.lastname: Wei
authors.affiliation: b College of Information Engineering, Shanghai Maritime University , Shanghai 201306 , P. R. China.
authors.forename: Shiwen
authors.initials: S
authors.lastname: Cheng
authors.affiliation: c Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences , Shanghai 200031 , P. R. China.
authors.forename: Xiangyin
authors.initials: X
authors.lastname: Kong
authors.affiliation: d State Key Laboratory of Drug Research, Drug Discovery and Design Center , Shanghai Institute of Materia Medica, Chinese Academy of Sciences , Shanghai 201203 , P. R. China.
authors.forename: Mingyue
authors.initials: M
authors.lastname: Zheng
authors.affiliation: c Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences , Shanghai 200031 , P. R. China.
authors.forename: Tao
authors.initials: T
authors.lastname: Huang
authors.affiliation: e School of Life Sciences, Shanghai University , Shanghai 200444 , P. R. China.
authors.forename: Yu-Dong
authors.initials: YD
authors.lastname: Cai
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D016208:Databases, Factual; D004347:Drug Interactions; D006801:Humans; D008962:Models, Theoretical; D011485:Protein Binding; D015203:Reproducibility of Results
keywords: Drug–drug interaction; chemical interaction; drug–target interaction; incremental feature selection; minimum redundancy maximum relevance; protein interaction