{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T13:44:46Z","timestamp":1781531086983,"version":"3.54.5"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,1,5]],"date-time":"2019-01-05T00:00:00Z","timestamp":1546646400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872220, 61572284"],"award-info":[{"award-number":["61872220, 61572284"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1186\/s12859-018-2575-6","type":"journal-article","created":{"date-parts":[[2019,1,5]],"date-time":"2019-01-05T06:16:49Z","timestamp":1546669009000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["The computational prediction of drug-disease interactions using the dual-network L2,1-CMF method"],"prefix":"10.1186","volume":"20","author":[{"given":"Zhen","family":"Cui","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying-Lian","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6104-2149","authenticated-orcid":false,"given":"Jin-Xing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junliang","family":"Shang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling-Yun","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,1,5]]},"reference":[{"issue":"3","key":"2575_CR1","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/nrd3078","volume":"9","author":"SM Paul","year":"2010","unstructured":"Paul SM, Mytelka DS, Dunwiddie CT, Persinger CC, Munos BH, Lindborg SR, Schacht AL. How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nat Rev Drug Discov. 2010;9(3):203\u201314.","journal-title":"Nat Rev Drug Discov"},{"issue":"6","key":"2575_CR2","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1186\/s12918-017-0480-7","volume":"11","author":"J-X Liu","year":"2017","unstructured":"Liu J-X, Wang D-Q, Zheng C-H, Gao Y-L, Wu S-S, Shang J-L. Identifying drug-pathway association pairs based on L2,1-integrative penalized matrix decomposition. BMC Syst Biol. 2017;11(6):119.","journal-title":"BMC Syst Biol"},{"issue":"19","key":"2575_CR3","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1186\/s12859-016-1377-y","volume":"17","author":"A Ezzat","year":"2016","unstructured":"Ezzat A, Wu M, Li X-L, Kwoh C-K. Drug-target interaction prediction via class imbalance-aware ensemble learning. BMC Bioinformatics. 2016;17(19):509.","journal-title":"BMC Bioinformatics"},{"issue":"5","key":"2575_CR4","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/j.tips.2013.03.004","volume":"34","author":"N Novac","year":"2013","unstructured":"Novac N. Challenges and opportunities of drug repositioning. Trends Pharmacol Sci. 2013;34(5):267\u201372.","journal-title":"Trends Pharmacol Sci"},{"issue":"Database issue","key":"2575_CR5","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1093\/nar\/gkp896","volume":"38","author":"M Kanehisa","year":"2010","unstructured":"Kanehisa M, Goto S, Furumichi M, Mao T, Hirakawa M. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 2010;38(Database issue):355\u201360.","journal-title":"Nucleic Acids Res"},{"issue":"Database issue","key":"2575_CR6","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1093\/nar\/gkt1207","volume":"42","author":"M Kuhn","year":"2014","unstructured":"Kuhn M, Szklarczyk D, Pletscher-Frankild S, Blicher TH, Von MC, Jensen LJ, Bork P. STITCH 4: integration of protein-chemical interactions with user data. Nucleic Acids Res. 2014;42(Database issue):401\u20137.","journal-title":"Nucleic Acids Res"},{"issue":"Database issue","key":"2575_CR7","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1093\/nar\/gkn665","volume":"37","author":"J Amberger","year":"2009","unstructured":"Amberger J, Bocchini CA, Scott AF, Hamosh A. McKusick\u2019s online Mendelian inheritance in man (OMIM). Nucleic Acids Res. 2009;37(Database issue):793\u20136.","journal-title":"Nucleic Acids Res"},{"issue":"Database issue","key":"2575_CR8","doi-asserted-by":"publisher","first-page":"D1035","DOI":"10.1093\/nar\/gkq1126","volume":"39","author":"C Knox","year":"2011","unstructured":"Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V. DrugBank 3.0: a comprehensive resource for \u2018omics\u2019 research on drugs. Nucleic Acids Res. 2011;39(Database issue):D1035.","journal-title":"Nucleic Acids Res"},{"issue":"Database issue","key":"2575_CR9","doi-asserted-by":"publisher","first-page":"1100","DOI":"10.1093\/nar\/gkr777","volume":"40","author":"A Gaulton","year":"2012","unstructured":"Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, Mcglinchey S, Michalovich D, Allazikani B. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 2012;40(Database issue):1100\u20137.","journal-title":"Nucleic Acids Res"},{"issue":"1\u20132","key":"2575_CR10","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/S1359-6446(05)03682-2","volume":"11","author":"DL Banville","year":"2006","unstructured":"Banville DL. Mining chemical structural information from the drug literature. Drug Discov Today. 2006;11(1\u20132):35\u201342.","journal-title":"Drug Discov Today"},{"key":"2575_CR11","doi-asserted-by":"publisher","first-page":"5501","DOI":"10.1038\/srep05501","volume":"4","author":"X Chen","year":"2014","unstructured":"Chen X, Yan GY. Semi-supervised learning for potential human microRNA-disease associations inference. Sci Rep. 2014;4:5501.","journal-title":"Sci Rep"},{"issue":"4","key":"2575_CR12","doi-asserted-by":"publisher","first-page":"596","DOI":"10.1197\/jamia.M3096","volume":"16","author":"H Yang","year":"2009","unstructured":"Yang H, Spasic I, Keane JA, Nenadic G. A text mining approach to the prediction of disease status from clinical discharge summaries. J Am Med Inform Assoc. 2009;16(4):596\u2013600.","journal-title":"J Am Med Inform Assoc"},{"issue":"10","key":"2575_CR13","doi-asserted-by":"publisher","first-page":"e111668","DOI":"10.1371\/journal.pone.0111668","volume":"9","author":"M Oh","year":"2014","unstructured":"Oh M, Ahn J, Yoon Y. A network-based classification model for deriving novel drug-disease associations and assessing their molecular actions. PLoS One. 2014;9(10):e111668.","journal-title":"PLoS One"},{"issue":"11","key":"2575_CR14","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1093\/bioinformatics\/bty013","volume":"34","author":"H Luo","year":"2018","unstructured":"Luo H, Li M, Wang S, Liu Q, Li Y, Wang J. Computational drug repositioning using low-rank matrix approximation and randomized algorithms. Bioinformatics. 2018;34(11):1904\u201312.","journal-title":"Bioinformatics"},{"issue":"21","key":"2575_CR15","doi-asserted-by":"publisher","first-page":"3624","DOI":"10.1093\/bioinformatics\/bty392","volume":"34","author":"L Zhang","year":"2018","unstructured":"Zhang L, Xiao M, Zhou J, Yu J. Lineage-associated underrepresented permutations (LAUPs) of mammalian genomic sequences based on a jellyfish-based LAUPs analysis application (JBLA). Bioinformatics. 2018;34(21):3624\u201330.","journal-title":"Bioinformatics"},{"key":"2575_CR16","first-page":"524821","volume":"2015","author":"J Shang","year":"2015","unstructured":"Shang J, Sun Y, Li S, Liu JX, Zheng CH, Zhang J. An improved opposition-based learning particle swarm optimization for the detection of SNP-SNP interactions. Biomed Res Int. 2015;2015:524821.","journal-title":"Biomed Res Int"},{"issue":"1","key":"2575_CR17","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1038\/msb.2011.26","volume":"7","author":"A Gottlieb","year":"2011","unstructured":"Gottlieb A, Stein GY, Ruppin E, Sharan R. PREDICT: a method for inferring novel drug indications with application to personalized medicine. Mol Syst Biol. 2011;7(1):496.","journal-title":"Mol Syst Biol"},{"issue":"1","key":"2575_CR18","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1186\/1758-2946-5-30","volume":"5","author":"F Napolitano","year":"2013","unstructured":"Napolitano F, Zhao Y, Moreira VM, Tagliaferri R, Kere J, D\u2019Amato M, Greco D. Drug repositioning: a machine-learning approach through data integration. J Cheminform. 2013;5(1):30.","journal-title":"J Cheminform"},{"issue":"20","key":"2575_CR19","doi-asserted-by":"publisher","first-page":"2923","DOI":"10.1093\/bioinformatics\/btu403","volume":"30","author":"W Wang","year":"2014","unstructured":"Wang W, Yang S, Zhang X, Li J. Drug repositioning by integrating target information through a heterogeneous network model. Bioinformatics. 2014;30(20):2923\u201330.","journal-title":"Bioinformatics"},{"issue":"1","key":"2575_CR20","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.artmed.2014.11.003","volume":"63","author":"V Mart\u00ednez","year":"2015","unstructured":"Mart\u00ednez V, Navarro C, Cano C, Fajardo W, Blanco A. DrugNet: network-based drug-disease prioritization by integrating heterogeneous data. Artif Intell Med. 2015;63(1):41\u20139.","journal-title":"Artif Intell Med"},{"issue":"17","key":"2575_CR21","doi-asserted-by":"publisher","first-page":"2664","DOI":"10.1093\/bioinformatics\/btw228","volume":"32","author":"H Luo","year":"2016","unstructured":"Luo H, Wang J, Li M, Luo J, Peng X, Wu FX, Pan Y. Drug repositioning based on comprehensive similarity measures and bi-random walk algorithm. Bioinformatics. 2016;32(17):2664.","journal-title":"Bioinformatics"},{"issue":"4","key":"2575_CR22","doi-asserted-by":"publisher","first-page":"1956","DOI":"10.1137\/080738970","volume":"20","author":"JF Cai","year":"2008","unstructured":"Cai JF, Cand S, Emmanuel J, Shen Z. A singular value thresholding algorithm for matrix completion. SIAM J Optim. 2008;20(4):1956\u201382.","journal-title":"SIAM J Optim"},{"issue":"9","key":"2575_CR23","doi-asserted-by":"publisher","first-page":"2562","DOI":"10.1021\/ci500340n","volume":"54","author":"J Yang","year":"2014","unstructured":"Yang J, Li Z, Fan X, Cheng Y. Drug\u2013disease association and drug-repositioning predictions in complex diseases using causal inference\u2013probabilistic matrix factorization. J Chem Inf Model. 2014;54(9):2562\u20139.","journal-title":"J Chem Inf Model"},{"issue":"18","key":"2575_CR24","doi-asserted-by":"publisher","first-page":"2304","DOI":"10.1093\/bioinformatics\/bts360","volume":"28","author":"M G\u00f6nen","year":"2012","unstructured":"G\u00f6nen M. Predicting drug\u2013target interactions from chemical and genomic kernels using Bayesian matrix factorization. Bioinformatics (Oxford, England). 2012;28(18):2304\u201310.","journal-title":"Bioinformatics (Oxford, England)"},{"issue":"9","key":"2575_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2017\/2498957","volume":"2017","author":"Z Shen","year":"2017","unstructured":"Shen Z, Zhang YH, Han K, Nandi AK, Honig B, Huang DS. miRNA-disease association prediction with collaborative matrix factorization. Complexity. 2017;2017(9):1\u20139.","journal-title":"Complexity"},{"issue":"3","key":"2575_CR26","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1109\/TCBB.2016.2530062","volume":"14","author":"A Ezzat","year":"2017","unstructured":"Ezzat A, Zhao P, Wu M, Li X-L, Kwoh C-K. Drug-target interaction prediction with graph regularized matrix factorization. IEEE\/ACM Trans Comput Biol Bioinformatics. 2017;14(3):646\u201356.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinformatics"},{"issue":"C","key":"2575_CR27","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.neucom.2016.09.083","volume":"228","author":"JX Liu","year":"2017","unstructured":"Liu JX, Wang D, Gao YL, Zheng CH, Shang JL, Liu F, Xu Y. A joint-L 2,1 -norm-constraint-based semi-supervised feature extraction for RNA-Seq data analysis. Neurocomputing. 2017;228(C):263\u20139.","journal-title":"Neurocomputing"},{"issue":"11","key":"2575_CR28","doi-asserted-by":"publisher","first-page":"2907","DOI":"10.1039\/C4MB00199K","volume":"10","author":"M Song","year":"2014","unstructured":"Song M, Yan Y, Jiang Z. Drug-pathway interaction prediction via multiple feature fusion. Mol BioSyst. 2014;10(11):2907\u201313.","journal-title":"Mol BioSyst"},{"issue":"21","key":"2575_CR29","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1002\/chin.200321205","volume":"34","author":"C Steinbeck","year":"2003","unstructured":"Christoph Steinbeck, \u2020, Yongquan Han, Stefan Kuhn, Oliver Horlacher, Edgar Luttmann A, Willighagen E: The chemistry development kit (CDK): an open-source Java library for chemo- and bioinformatics. Cheminform 2003, 34(21):493\u2013500.","journal-title":"Cheminform"},{"issue":"5","key":"2575_CR30","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1038\/sj.ejhg.5201585","volume":"14","author":"MA Driel","year":"2006","unstructured":"Driel MA, Van JB, Gert V, Han G, Brunner LJAM. A text-mining analysis of the human phenome. Eur J Hum Genet. 2006;14(5):535\u201342.","journal-title":"Eur J Hum Genet"},{"issue":"15","key":"2575_CR31","doi-asserted-by":"publisher","first-page":"2595","DOI":"10.1093\/bioinformatics\/btv153","volume":"31","author":"J Grau","year":"2015","unstructured":"Grau J, Grosse I, Keilwagen J. PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R. Bioinformatics. 2015;31(15):2595\u20137.","journal-title":"Bioinformatics"},{"issue":"8","key":"2575_CR32","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett T. An introduction to ROC analysis. Pattern Recogn Lett. 2006;27(8):861\u201374.","journal-title":"Pattern Recogn Lett"},{"key":"2575_CR33","doi-asserted-by":"crossref","unstructured":"Ezzat A, Wu M, Li XL, Kwoh CK. Computational prediction of drug-target interactions using chemogenomic approaches: an empirical survey. Brief Bioinform. 2018;8.","DOI":"10.1093\/bib\/bby002"},{"key":"2575_CR34","doi-asserted-by":"crossref","unstructured":"Laarhoven TV, Nabuurs SB, Marchiori E. Gaussian interaction profile kernels for predicting drug\u2013target interaction. Bioinformatics. 2011;27(21):3036\u201343.","DOI":"10.1093\/bioinformatics\/btr500"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-018-2575-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12859-018-2575-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-018-2575-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,1,4]],"date-time":"2020-01-04T19:04:39Z","timestamp":1578164679000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-018-2575-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,5]]},"references-count":34,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["2575"],"URL":"https:\/\/doi.org\/10.1186\/s12859-018-2575-6","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,5]]},"assertion":[{"value":"21 August 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 December 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 January 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Publisher\u2019s Note"}}],"article-number":"5"}}