{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T07:24:39Z","timestamp":1782631479632,"version":"3.54.5"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2016,10,28]],"date-time":"2016-10-28T00:00:00Z","timestamp":1477612800000},"content-version":"vor","delay-in-days":139,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,6,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Identifying drug\u2013target 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\u2013target interactions of new candidate drugs or targets.<\/jats:p>\n               <jats:p>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.<\/jats:p>\n               <jats:p>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.<\/jats:p>\n               <jats:p>Availability: \u00a0http:\/\/datamining-iip.fudan.edu.cn\/service\/DrugE-Rank<\/jats:p>\n               <jats:p>Contact: \u00a0zhusf@fudan.edu.cn<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btw244","type":"journal-article","created":{"date-parts":[[2016,6,15]],"date-time":"2016-06-15T15:43:52Z","timestamp":1466005432000},"page":"i18-i27","source":"Crossref","is-referenced-by-count":132,"title":["DrugE-Rank: improving drug\u2013target interaction prediction of new candidate drugs or targets by ensemble learning to rank"],"prefix":"10.1093","volume":"32","author":[{"given":"Qingjun","family":"Yuan","sequence":"first","affiliation":[{"name":"1 School of Computer Science, Fudan University, Shanghai, China"},{"name":"2 Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junning","family":"Gao","sequence":"additional","affiliation":[{"name":"1 School of Computer Science, Fudan University, Shanghai, China"},{"name":"2 Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongliang","family":"Wu","sequence":"additional","affiliation":[{"name":"1 School of Computer Science, Fudan University, Shanghai, China"},{"name":"2 Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shihua","family":"Zhang","sequence":"additional","affiliation":[{"name":"3 National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroshi","family":"Mamitsuka","sequence":"additional","affiliation":[{"name":"4 Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan"},{"name":"5 Department of Computer Science, Aalto University, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanfeng","family":"Zhu","sequence":"additional","affiliation":[{"name":"1 School of Computer Science, Fudan University, Shanghai, China"},{"name":"2 Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China"},{"name":"6 Centre for Computational System Biology, Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2016,6,11]]},"reference":[{"key":"2023020112304944900_btw244-B1","doi-asserted-by":"crossref","first-page":"10","DOI":"10.2174\/157340911793743547","article-title":"Recent advances in ligand-based drug design: relevance and utility of the conformationally sampled pharmacophore approach","volume":"7","author":"Acharya","year":"2011","journal-title":"Curr. Comput.-Aided Drug Des"},{"key":"2023020112304944900_btw244-B2","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1021\/ci9003865","article-title":"Ranking chemical structures for drug discovery: a new machine learning approach","volume":"50","author":"Agarwal","year":"2010","journal-title":"J. Chem. Inf. Model"},{"key":"2023020112304944900_btw244-B3","doi-asserted-by":"crossref","first-page":"2397","DOI":"10.1093\/bioinformatics\/btp433","article-title":"Supervised prediction of drug\u2013target interactions using bipartite local models","volume":"25","author":"Bleakley","year":"2009","journal-title":"Bioinformatics"},{"key":"2023020112304944900_btw244-B4","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/S1574-1400(08)00012-1","article-title":"Pubchem: integrated platform of small molecules and biological activities","volume":"4","author":"Bolton","year":"2008","journal-title":"Ann. Rep. Comput. Chem"},{"key":"2023020112304944900_btw244-B5","first-page":"89","article-title":"Uniprotkb\/swiss-prot","volume":"406","author":"Boutet","year":"2007","journal-title":"Methods Mol. Biol"},{"key":"2023020112304944900_btw244-B6","author":"Burges","year":"2010"},{"key":"2023020112304944900_btw244-B7","first-page":"233","article-title":"The relationship between precision-recall and roc curves","volume-title":"Machine Learning, Proceedings of the Twenty-Third International Conference ICML 2006","author":"Davis","year":"2006"},{"key":"2023020112304944900_btw244-B8","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1093\/bib\/bbt056","article-title":"Similarity-based machine learning methods for predicting drug\u2013target interactions: a brief review","volume":"15","author":"Ding","year":"2014","journal-title":"Brief. Bioinfo"},{"key":"2023020112304944900_btw244-B9","doi-asserted-by":"crossref","first-page":"2304","DOI":"10.1093\/bioinformatics\/bts360","article-title":"Predicting drug\u2013target interactions from chemical and genomic kernels using bayesian matrix factorization","volume":"28","author":"G\u00f6nen","year":"2012","journal-title":"Bioinformatics"},{"key":"2023020112304944900_btw244-B10","doi-asserted-by":"crossref","first-page":"2149","DOI":"10.1093\/bioinformatics\/btn409","article-title":"Protein\u2013ligand interaction prediction: an improved chemogenomics approach","volume":"24","author":"Jacob","year":"2008","journal-title":"Bioinformatics"},{"key":"2023020112304944900_btw244-B11","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1038\/nature08506","article-title":"Predicting new molecular targets for known drugs","volume":"462","author":"Keiser","year":"2009","journal-title":"Nature"},{"key":"2023020112304944900_btw244-B12","doi-asserted-by":"crossref","first-page":"D1, D1091","DOI":"10.1093\/nar\/gkt1068","article-title":"Drugbank 4.0: shedding new light on drug metabolism","volume":"42","author":"Law","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2023020112304944900_btw244-B13","doi-asserted-by":"crossref","first-page":"1854","DOI":"10.1587\/transinf.E94.D.1854","article-title":"A short introduction to learning to rank","volume":"94-D","author":"Li","year":"2011","journal-title":"IEICE Trans"},{"key":"2023020112304944900_btw244-B14","doi-asserted-by":"crossref","first-page":"3492","DOI":"10.1093\/bioinformatics\/btv413","article-title":"Application of learning to rank to protein remote homology detection","volume":"31","author":"Liu","year":"2015","journal-title":"Bioinformatics"},{"key":"2023020112304944900_btw244-B15","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1093\/bioinformatics\/btv237","article-title":"Meshlabeler: improving the accuracy of large-scale mesh indexing by integrating diverse evidence","volume":"31","author":"Liu","year":"2015","journal-title":"Bioinformatics"},{"key":"2023020112304944900_btw244-B16","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1561\/1500000016","article-title":"Learning to rank for information retrieval","volume":"3","author":"Liu","year":"2009","journal-title":"Found. Trends Inf. Retriev"},{"key":"2023020112304944900_btw244-B17","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1038\/nature11159","article-title":"Large-scale prediction and testing of drug activity on side-effect targets","volume":"486","author":"Lounkine","year":"2012","journal-title":"Nature"},{"key":"2023020112304944900_btw244-B18","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1016\/S1359-6446(02)02483-2","article-title":"Structure-based virtual screening: an overview","volume":"7","author":"Lyne","year":"2002","journal-title":"Drug Discov. Today"},{"key":"2023020112304944900_btw244-B19","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1093\/bioinformatics\/bts670","article-title":"Drug\u2013target interaction prediction by learning from local information and neighbors","volume":"29","author":"Mei","year":"2013","journal-title":"Bioinformatics"},{"key":"2023020112304944900_btw244-B20","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1517\/17425255.2014.950222","article-title":"Drug-target interaction prediction via chemogenomic space: learning based methods","volume":"10","author":"Mousavian","year":"2014","journal-title":"Expert Opin. Drug Metabol. Toxicol"},{"key":"2023020112304944900_btw244-B21","doi-asserted-by":"crossref","first-page":"2004","DOI":"10.1093\/bioinformatics\/btm266","article-title":"Statistical prediction of protein\u2013chemical interactions based on chemical structure and mass spectrometry data","volume":"23","author":"Nagamine","year":"2007","journal-title":"Bioinformatics"},{"key":"2023020112304944900_btw244-B22","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.drudis.2011.06.013","article-title":"Target-drug interations: first principles and their application to drug discovery","volume":"17","author":"Nunez","year":"2012","journal-title":"Drug Discov. Today"},{"key":"2023020112304944900_btw244-B23","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1038\/nrd2199","article-title":"How many drug targets are there","volume":"5","author":"Overington","year":"2006","journal-title":"Nat. Rev. Drug Discov"},{"key":"2023020112304944900_btw244-B24","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.jprot.2014.05.011","article-title":"Improved prediction of peptide detectability for targeted proteomics using a rank-based algorithm and organism-specific data","volume":"108","author":"Qeli","year":"2014","journal-title":"J. Proteomics"},{"key":"2023020112304944900_btw244-B25","doi-asserted-by":"crossref","first-page":"W385","DOI":"10.1093\/nar\/gkr284","article-title":"Update of profeat: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence","volume":"39(suppl 2)","author":"Rao","year":"2011","journal-title":"Nucleic Acids Res"},{"key":"2023020112304944900_btw244-B26","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1021\/ci100308f","article-title":"Structrank: a new approach for ligand-based virtual screening","volume":"51","author":"Rathke","year":"2010","journal-title":"J. Chem. Inf. Model"},{"key":"2023020112304944900_btw244-B27","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1038\/nrd3681","article-title":"Diagnosing the decline in pharmaceutical r&d efficiency","volume":"11","author":"Scannell","year":"2012","journal-title":"Nat. Rev. Drug Discov"},{"key":"2023020112304944900_btw244-B28","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.ymeth.2015.04.036","article-title":"Predicting drug\u2013target interaction for new drugs using enhanced similarity measures and super-target clustering","volume":"83","author":"Shi","year":"2015","journal-title":"Methods"},{"key":"2023020112304944900_btw244-B29","doi-asserted-by":"crossref","first-page":"i487","DOI":"10.1093\/bioinformatics\/bts412","article-title":"Identification of chemogenomic features from drug\u2013target interaction networks using interpretable classifiers","volume":"28","author":"Tabei","year":"2012","journal-title":"Bioinformatics"},{"key":"2023020112304944900_btw244-B30","doi-asserted-by":"crossref","first-page":"3036","DOI":"10.1093\/bioinformatics\/btr500","article-title":"Gaussian interaction profile kernels for predicting drug\u2013target interaction","volume":"27","author":"van Laarhoven","year":"2011","journal-title":"Bioinformatics"},{"key":"2023020112304944900_btw244-B31","doi-asserted-by":"crossref","first-page":"e66952.","DOI":"10.1371\/journal.pone.0066952","article-title":"Predicting drug-target interactions for new drug compounds using a weighted nearest neighbor profile","volume":"8","author":"van Laarhoven","year":"2013","journal-title":"PloS One"},{"key":"2023020112304944900_btw244-B32","doi-asserted-by":"crossref","first-page":"S6","DOI":"10.1186\/1752-0509-4-S2-S6","article-title":"Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces","volume":"4","author":"Xia","year":"2010","journal-title":"BMC Syst. Biol"},{"key":"2023020112304944900_btw244-B33","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1038\/msb.2011.5","article-title":"Analysis of multiple compound\u2013protein interactions reveals novel bioactive molecules","volume":"7","author":"Yabuuchi","year":"2011","journal-title":"Mol. Syst. Biol"},{"key":"2023020112304944900_btw244-B34","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/978-1-62703-107-3_9","article-title":"Chemogenomic approaches to infer drug-target interaction networks","volume":"939","author":"Yamanishi","year":"2013","journal-title":"Methods Mol. Biol"},{"key":"2023020112304944900_btw244-B35","doi-asserted-by":"crossref","first-page":"i232","DOI":"10.1093\/bioinformatics\/btn162","article-title":"Prediction of drug\u2013target interaction networks from the integration of chemical and genomic spaces","volume":"24","author":"Yamanishi","year":"2008","journal-title":"Bioinformatics"},{"key":"2023020112304944900_btw244-B36","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s13321-015-0052-z","article-title":"When drug discovery meets web search: learning to rank for ligand-based virtual screening","volume":"7","author":"Zhang","year":"2015","journal-title":"J. Cheminf"},{"key":"2023020112304944900_btw244-B37","first-page":"1025","volume-title":"ACM KDD","author":"Zheng","year":"2013"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/32\/12\/i18\/49020658\/bioinformatics_32_12_i18.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/32\/12\/i18\/49020658\/bioinformatics_32_12_i18.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T22:36:25Z","timestamp":1675290985000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/32\/12\/i18\/2288576"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,6,11]]},"references-count":37,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2016,6,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btw244","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2016,6,15]]},"published":{"date-parts":[[2016,6,11]]}}}