Analysis and prediction of drug-drug interaction by minimum redundancy maximum relevance and incremental feature selection
文献类型：期刊论文
| 作者 | Liu, Lili2; Chen, Lei3; Wei, Lai3; Cheng, Shiwen3; Zhang, Yu-Hang1; Kong, Xiangyin1; Huang, Tao1; Zheng, Mingyue4; Cai, Yu-Dong5; , |
| 刊名 | JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS
 |
| 出版日期 | 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. |
| 学科主题 | Biochemistry & Molecular Biology ; Biophysics |
| WOS关键词 | IN-VITRO DATA ; AMINO-ACID-COMPOSITION ; ACETOHYDROXYACID SYNTHASE ; RANDOM FORESTS ; PROTEINS ; INHIBITOR ; BINDING ; DISEASE ; AGENTS ; CYP3A4 |
| 语种 | 英语 |
| WOS记录号 | WOS:000392858300006 |
| 出版者 | TAYLOR & FRANCIS INC |
| 版本 | 出版稿 |
| 源URL | [http://202.127.25.144/handle/331004/786]  |
| 专题 | 中国科学院上海生命科学研究院营养科学研究所 |
| 作者单位 | 1.Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Hlth Sci, Shanghai 200031, Peoples R China; 2.Chinese Acad Sci, Shanghai Inst Mat Med, Intelligence Res Dept, Informat Ctr, Shanghai 201203, Peoples R China; 3.Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, 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,Wei, Lai,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.,Wei, Lai.,Cheng, Shiwen.,Zhang, Yu-Hang.,...&,.(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. |
入库方式： OAI收割
来源：上海营养与健康研究所
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