DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank
文献类型：期刊论文
| 作者 | Yuan, Qingjun1,2; Gao, Junning1,2; Wu, Dongliang1,2; Zhang, Shihua3 ; Mamitsuka, Hiroshi4,5; Zhu, Shanfeng1,2,6
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| 刊名 | BIOINFORMATICS
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| 出版日期 | 2016-06-15 |
| 卷号 | 32期号:12页码:18-27 |
| ISSN号 | 1367-4803 |
| DOI | 10.1093/bioinformatics/btw244 |
| 英文摘要 | Motivation: Identifying drug-target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although these approaches have used many different principles, their performance is far from satisfactory, especially in predicting drug-target interactions of new candidate drugs or targets. Methods: Approaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. Learning to rank is the most powerful technique in the feature-based methods. Similarity-based methods are well accepted, due to their idea of connecting the chemical and genomic spaces, represented by drug and target similarities, respectively. We propose a new method, DrugE-Rank, to improve the prediction performance by nicely combining the advantages of the two different types of methods. That is, DrugE-Rank uses LTR, for which multiple well-known similarity-based methods can be used as components of ensemble learning. Results: The performance of DrugE-Rank is thoroughly examined by three main experiments using data from DrugBank: (i) cross-validation on FDA (US Food and Drug Administration) approved drugs before March 2014; (ii) independent test on FDA approved drugs after March 2014; and (iii) independent test on FDA experimental drugs. Experimental results show that DrugE-Rank outperforms competing methods significantly, especially achieving more than 30% improvement in Area under Prediction Recall curve for FDA approved new drugs and FDA experimental drugs. |
| WOS研究方向 | Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics |
| 语种 | 英语 |
| WOS记录号 | WOS:000379734300003 |
| 出版者 | OXFORD UNIV PRESS |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/23117]  |
| 专题 | 应用数学研究所 |
| 通讯作者 | Zhu, Shanfeng |
| 作者单位 | 1.Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China 2.Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Natl Ctr Math & Interdisciplinary Sci, Beijing, Peoples R China 4.Kyoto Univ, Inst Chem Res, Bioinformat Ctr, Uji, Japan 5.Aalto Univ, Dept Comp Sci, Espoo, Finland 6.Fudan Univ, Ctr Computat Syst Biol, Shanghai, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yuan, Qingjun,Gao, Junning,Wu, Dongliang,et al. DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank[J]. BIOINFORMATICS,2016,32(12):18-27. |
| APA | Yuan, Qingjun,Gao, Junning,Wu, Dongliang,Zhang, Shihua,Mamitsuka, Hiroshi,&Zhu, Shanfeng.(2016).DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank.BIOINFORMATICS,32(12),18-27. |
| MLA | Yuan, Qingjun,et al."DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank".BIOINFORMATICS 32.12(2016):18-27. |
入库方式： OAI收割
来源：数学与系统科学研究院
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