DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank

doi:10.1093/bioinformatics/btw244

CSpace > 应用数学研究所

	DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank
	Yuan, Qingjun 1,2; Gao, Junning 1,2; Wu, Dongliang 1,2; Zhang, Shihua3 ; Mamitsuka, Hiroshi 4,5; Zhu, Shanfeng 1,2,6
	2016-06-15
发表期刊	BIOINFORMATICS
ISSN	1367-4803
卷号	32 期号:12 页码:18-27
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.
DOI	10.1093/bioinformatics/btw244
语种	英语
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
WOS类目	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS记录号	WOS:000379734300003
出版者	OXFORD UNIV PRESS
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/23118
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.