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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Yuan, Qingjun
dc.contributor.author Gao, Junning
dc.contributor.author Wu, Dongliang
dc.contributor.author Zhang, Shihua
dc.contributor.author Mamitsuka, Hiroshi
dc.contributor.author Zhu, Shanfeng
dc.date.accessioned 2016-10-13T06:17:37Z
dc.date.issued 2016-06-15
dc.identifier.citation Yuan , Q , Gao , J , Wu , D , Zhang , S , Mamitsuka , H & Zhu , S 2016 , ' DrugE-Rank : Improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank ' BIOINFORMATICS , vol 32 , no. 12 , pp. i18-i27 . DOI: 10.1093/bioinformatics/btw244 en
dc.identifier.issn 1367-4803
dc.identifier.issn 1460-2059
dc.identifier.other PURE UUID: f3e5debf-5ea4-45c8-8726-598367dc82d1
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/drugerank(f3e5debf-5ea4-45c8-8726-598367dc82d1).html
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=84976517528&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/6470497/i18.full.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/22971
dc.description.abstract 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 ascomponents 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. en
dc.format.extent 10
dc.format.extent i18-i27
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries BIOINFORMATICS en
dc.relation.ispartofseries Volume 32, issue 12 en
dc.rights openAccess en
dc.subject.other Biochemistry en
dc.subject.other Molecular Biology en
dc.subject.other Computational Theory and Mathematics en
dc.subject.other Computer Science Applications en
dc.subject.other Computational Mathematics en
dc.subject.other Statistics and Probability en
dc.subject.other 113 Computer and information sciences en
dc.subject.other Computational data analysis en
dc.title DrugE-Rank en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Fudan University
dc.contributor.department CAS - Academy of Mathematics and System Sciences
dc.contributor.department Department of Computer Science
dc.subject.keyword drug-target interactions
dc.subject.keyword ensemble learning
dc.subject.keyword learning to rank
dc.subject.keyword Biochemistry
dc.subject.keyword Molecular Biology
dc.subject.keyword Computational Theory and Mathematics
dc.subject.keyword Computer Science Applications
dc.subject.keyword Computational Mathematics
dc.subject.keyword Statistics and Probability
dc.subject.keyword 113 Computer and information sciences
dc.subject.keyword Computational data analysis
dc.identifier.urn URN:NBN:fi:aalto-201610135071
dc.identifier.doi 10.1093/bioinformatics/btw244
dc.type.version publishedVersion

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