Analysis and prediction of drug-drug interaction by minimum redundancy maximum relevance and incremental feature selection

doi:10.1080/07391102.2016.1138142

CORC > 上海药物研究所 > 中国科学院上海药物研究所 > 药物发现与设计中心

	Analysis and prediction of drug-drug interaction by minimum redundancy maximum relevance and incremental feature selection
	Liu, Lili 1; Chen, Lei 2; Zhang, Yu-Hang 3; Wei, Lai 2; Cheng, Shiwen 2; Kong, Xiangyin 3; Zheng, Mingyue4 ; Huang, Tao 3; Cai, Yu-Dong 5
刊名	JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS
	2017
卷号	35 期号:2 页码:312-329
关键词	Drug-drug interaction drug-target interaction chemical interaction protein interaction minimum redundancy maximum relevance incremental feature selection
ISSN号	0739-1102
DOI	10.1080/07391102.2016.1138142
文献子类	Article
英文摘要	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.
资助项目	National Natural Science Foundation of China[31371335] ; National Natural Science Foundation of China[11371008] ; National Natural Science Foundation of China[61202021] ; National Natural Science Foundation of China[61203240] ; National Natural Science Foundation of China[61303099] ; National Natural Science Foundation of China[61373028] ; Innovation Program of Shanghai Municipal Education Commission[12ZZ087] ; Innovation Program of Shanghai Municipal Education Commission[14YZ102] ; National Basic Research Program of China[2011CB510101] ; National Basic Research Program of China[2011CB510102] ; Shanghai Educational Development Foundation[12CG55] ; "The First-class Discipline of Universities in Shanghai"[00000000]
WOS关键词	IN-VITRO DATA ; AMINO-ACID-COMPOSITION ; ACETOHYDROXYACID SYNTHASE ; RANDOM FORESTS ; PROTEINS ; INHIBITOR ; BINDING ; DISEASE ; AGENTS ; CYP3A4
WOS研究方向	Biochemistry & Molecular Biology ; Biophysics
语种	英语
出版者	TAYLOR & FRANCIS INC
WOS记录号	WOS:000392858300006
内容类型	期刊论文
源URL	[http://119.78.100.183/handle/2S10ELR8/275745]
专题	药物发现与设计中心中科院受体结构与功能重点实验室信息中心新药研究国家重点实验室
通讯作者	Chen, Lei; Zheng, Mingyue; Huang, Tao; Cai, Yu-Dong
作者单位	1.Chinese Acad Sci, Shanghai Inst Mat Med, Intelligence Res Dept, Informat Ctr, Shanghai 201203, Peoples R China; 2.Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China; 3.Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Hlth Sci, Shanghai 200031, Peoples R China; 4.Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, Shanghai 201203, Peoples R China; 5.Shanghai Univ, Sch Life Sci, Shanghai 200444, Peoples R China
推荐引用方式 GB/T 7714	Liu, Lili,Chen, Lei,Zhang, Yu-Hang,et al. Analysis and prediction of drug-drug interaction by minimum redundancy maximum relevance and incremental feature selection[J]. JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS,2017,35(2):312-329.
APA	Liu, Lili.,Chen, Lei.,Zhang, Yu-Hang.,Wei, Lai.,Cheng, Shiwen.,...&Cai, Yu-Dong.(2017).Analysis and prediction of drug-drug interaction by minimum redundancy maximum relevance and incremental feature selection.JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS,35(2),312-329.
MLA	Liu, Lili,et al."Analysis and prediction of drug-drug interaction by minimum redundancy maximum relevance and incremental feature selection".JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS 35.2(2017):312-329.