Identification of Drug-Drug Interactions Using Chemical Interactions
文献类型：期刊论文
| 作者 | Chen, Lei2; Chu, Chen3; Zhang, Yu-Hang4; Kong, Xiangyin4; Huang, Tao4; Zheng, Mingyue1; Zhu, LiuCun5; , |
| 刊名 | CURRENT BIOINFORMATICS
 |
| 出版日期 | 2017 |
| 卷号 | 12期号:6页码:526-534 |
| 关键词 | Drug-drug interaction chemical interaction chemical structure similarity nearest neighbor algorithm majority voting imbalanced dataset |
| ISSN号 | 1574-8936 |
| DOI | 10.2174/1574893611666160618094219 |
| 文献子类 | Article |
| 英文摘要 | Background: One drug can affect the activity of another when they are administered together, which can cause adverse drug reactions or sometimes improve therapeutic effects. Therefore, correct identification of drug-drug interactions (DDIs) can help medical workers use various drugs effectively, avoiding adverse effects and improving therapeutic effects. Methods: This study proposed a novel prediction model to identify DDIs. A new metric was constructed to evaluate the similarity of two pairs of drugs using chemical interaction information retrieved from STITCH. Validated DDIs retrieved from DrugBank were employed, from which we constructed all possible pairs of drugs that were deemed as negative samples. The whole dataset was divided into one training dataset and one test dataset. To address the imbalanced data, a complicated dataset compilation strategy was adopted to construct nine training datasets from the original training dataset, reducing the ratio of positive samples and negative samples. Nine predictors based on the nearest neighbor algorithm were built based on these training datasets. The proposed model integrated the above nine predictors by majority voting and its performance was evaluated on the test dataset. Results: The predicted results indicate that the method is quite effective for identification of DDIs. Finally, we also discussed the ability of the method for identifying novel DDIs by investigating the likelihood of some negative samples in the test dataset that were predicted as DDIs being novel DDIs. Conclusion: The proposed method has a good ability for identification of potential DDIs. |
| 学科主题 | Biochemistry & Molecular Biology ; Mathematical & Computational Biology |
| WOS关键词 | MUSCARINIC M2 RECEPTORS ; IN-VITRO ; PHARMACOKINETIC INTERACTION ; OLANZAPINE TREATMENT ; PREDICTION ; CARBAMAZEPINE ; BUPIVACAINE ; INFORMATION ; PROTEINS ; RATS |
| 语种 | 英语 |
| WOS记录号 | WOS:000418706100006 |
| 出版者 | BENTHAM SCIENCE PUBL LTD |
| 版本 | 出版稿 |
| 源URL | [http://202.127.25.144/handle/331004/1056]  |
| 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 Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, 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 Biochem & Cell Biol, Shanghai 200031, Peoples R China; 4.Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Hlth Sci, Shanghai 200031, Peoples R China; 5.Shanghai Univ, Coll Life Sci, Shanghai 200444, Peoples R China, |
| 推荐引用方式 GB/T 7714 | Chen, Lei,Chu, Chen,Zhang, Yu-Hang,et al. Identification of Drug-Drug Interactions Using Chemical Interactions[J]. CURRENT BIOINFORMATICS,2017,12(6):526-534. |
| APA | Chen, Lei.,Chu, Chen.,Zhang, Yu-Hang.,Kong, Xiangyin.,Huang, Tao.,...&,.(2017).Identification of Drug-Drug Interactions Using Chemical Interactions.CURRENT BIOINFORMATICS,12(6),526-534. |
| MLA | Chen, Lei,et al."Identification of Drug-Drug Interactions Using Chemical Interactions".CURRENT BIOINFORMATICS 12.6(2017):526-534. |
入库方式： OAI收割
来源：上海营养与健康研究所
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