{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T07:11:49Z","timestamp":1773472309154,"version":"3.50.1"},"reference-count":63,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2020,1,12]],"date-time":"2020-01-12T00:00:00Z","timestamp":1578787200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100010581","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2019ZDPY01"],"award-info":[{"award-number":["2019ZDPY01"]}],"id":[{"id":"10.13039\/501100010581","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,1,18]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Emerging evidence shows that microRNAs (miRNAs) play a critical role in diverse fundamental and important biological processes associated with human diseases. Inferring potential disease related miRNAs and employing them as the biomarkers or drug targets could contribute to the prevention, diagnosis and treatment of complex human diseases. In view of that traditional biological experiments cost much time and resources, computational models would serve as complementary means to uncover potential miRNA\u2013disease associations. In this study, we proposed a new computational model named Neighborhood Constraint Matrix Completion for MiRNA\u2013Disease Association prediction (NCMCMDA) to predict potential miRNA\u2013disease associations. The main task of NCMCMDA was to recover the missing miRNA\u2013disease associations based on the known miRNA\u2013disease associations and integrated disease (miRNA) similarity. In this model, we innovatively integrated neighborhood constraint with matrix completion, which provided a novel idea of utilizing similarity information to assist the prediction. After the recovery task was transformed into an optimization problem, we solved it with a fast iterative shrinkage-thresholding algorithm. As a result, the AUCs of NCMCMDA in global and local leave-one-out cross validation were 0.9086 and 0.8453, respectively. In 5-fold cross validation, NCMCMDA achieved an average AUC of 0.8942 and standard deviation of 0.0015, which demonstrated NCMCMDA\u2019s superior performance than many previous computational methods. Furthermore, NCMCMDA was applied to three different types of case studies to further evaluate its prediction reliability and accuracy. As a result, 84% (colon neoplasms), 98% (esophageal neoplasms) and 98% (breast neoplasms) of the top 50 predicted miRNAs were verified by recent literature.<\/jats:p>","DOI":"10.1093\/bib\/bbz159","type":"journal-article","created":{"date-parts":[[2019,11,12]],"date-time":"2019-11-12T20:09:25Z","timestamp":1573589365000},"page":"485-496","source":"Crossref","is-referenced-by-count":180,"title":["NCMCMDA: miRNA\u2013disease association prediction through neighborhood constraint matrix completion"],"prefix":"10.1093","volume":"22","author":[{"given":"Xing","family":"Chen","sequence":"first","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology"}]},{"given":"Lian-Gang","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology"}]},{"given":"Yan","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology"}]}],"member":"286","published-online":{"date-parts":[[2020,1,12]]},"reference":[{"key":"2021012203431344100_ref1","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1016\/S0092-8674(01)00616-X","article-title":"microRNAs: tiny regulators with great potential","volume":"107","author":"Ambros","year":"2001","journal-title":"Cell"},{"key":"2021012203431344100_ref2","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/S0092-8674(04)00045-5","article-title":"MicroRNAs: genomics, biogenesis, mechanism, and function","volume":"116","author":"Bartel","year":"2004","journal-title":"Cell"},{"key":"2021012203431344100_ref3","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.cell.2009.01.002","article-title":"MicroRNAs: target recognition and regulatory functions","volume":"136","author":"Bartel","year":"2009","journal-title":"Cell"},{"key":"2021012203431344100_ref4","doi-asserted-by":"crossref","first-page":"1290","DOI":"10.1093\/nar\/gki200","article-title":"Antisense inhibition of human miRNAs and indications for an involvement of miRNA in cell growth and apoptosis","volume":"33","author":"Cheng","year":"2005","journal-title":"Nucleic Acids Res"},{"key":"2021012203431344100_ref5","doi-asserted-by":"crossref","first-page":"1288","DOI":"10.1126\/science.1121566","article-title":"Encountering microRNAs in cell fate signaling","volume":"310","author":"Karp","year":"2005","journal-title":"Science"},{"key":"2021012203431344100_ref6","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1038\/nrc1840","article-title":"Oncomirs\u2014microRNAs with a role in cancer","volume":"6","author":"Esquela-Kerscher","year":"2006","journal-title":"Nat Rev Cancer"},{"key":"2021012203431344100_ref7","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1038\/nrc1997","article-title":"MicroRNA signatures in human cancers","volume":"6","author":"Calin","year":"2006","journal-title":"Nat Rev Cancer"},{"key":"2021012203431344100_ref8","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1038\/nrg.2016.134","article-title":"A network-biology perspective of microRNA function and dysfunction in cancer","volume":"17","author":"Bracken","year":"2016","journal-title":"Nat Rev Genet"},{"key":"2021012203431344100_ref9","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1038\/onc.2012.636","article-title":"Upregulation of miRNA-155 promotes tumour angiogenesis by targeting VHL and is associated with poor prognosis and triple-negative breast cancer","volume":"33","author":"Kong","year":"2014","journal-title":"Oncogene"},{"key":"2021012203431344100_ref10","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1093\/cvr\/cvn156","article-title":"Role of microRNAs in vascular diseases, inflammation, and angiogenesis","volume":"79","author":"Urbich","year":"2008","journal-title":"Cardiovasc Res"},{"key":"2021012203431344100_ref11","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1161\/CIRCRESAHA.107.163147","article-title":"Emerging role of MicroRNAs in cardiovascular biology","volume":"101","author":"Latronico","year":"2007","journal-title":"Circ Res"},{"key":"2021012203431344100_ref12","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1038\/nature09783","article-title":"Pervasive roles of microRNAs in cardiovascular biology","volume":"469","author":"Small","year":"2011","journal-title":"Nature"},{"key":"2021012203431344100_ref13","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1038\/nature06174","article-title":"Tumour invasion and metastasis initiated by microRNA-10b in breast cancer","volume":"449","author":"Ma","year":"2007","journal-title":"Nature"},{"key":"2021012203431344100_ref14","doi-asserted-by":"crossref","first-page":"4914","DOI":"10.1038\/onc.2010.237","article-title":"miR-23b targets proline oxidase, a novel tumor suppressor protein in renal cancer","volume":"29","author":"Liu","year":"2010","journal-title":"Oncogene"},{"key":"2021012203431344100_ref15","first-page":"10","article-title":"Therapeutic role of miR-19a\/19b in cardiac regeneration and protection from myocardial infarction","volume":"2019","author":"Gao","year":"1802","journal-title":"Nat Commun"},{"key":"2021012203431344100_ref16","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1038\/nrc1997","article-title":"MicroRNA signatures in human cancers","volume":"6","author":"Calin","year":"2006","journal-title":"Nat Rev Cancer"},{"key":"2021012203431344100_ref17","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1093\/bib\/bbx130","article-title":"MicroRNAs and complex diseases: from experimental results to computational models","volume":"20","author":"Chen","year":"2019","journal-title":"Brief Bioinform"},{"issue":"S2","key":"2021012203431344100_ref18","article-title":"Prioritization of disease microRNAs through a human phenome-microRNAome network","volume":"4","author":"Jiang","year":"2010","journal-title":"BMC Syst Biol"},{"key":"2021012203431344100_ref19","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1093\/bioinformatics\/btt677","article-title":"Protein-driven inference of miRNA-disease associations","volume":"30","author":"Mork","year":"2014","journal-title":"Bioinformatics"},{"key":"2021012203431344100_ref20","doi-asserted-by":"crossref","first-page":"27036","DOI":"10.1038\/srep27036","article-title":"Prediction of miRNA-disease associations with a vector space model","volume":"6","author":"Pasquier","year":"2016","journal-title":"Sci Rep"},{"key":"2021012203431344100_ref21","doi-asserted-by":"crossref","first-page":"e70204","DOI":"10.1371\/journal.pone.0070204","article-title":"Prediction of microRNAs associated with human diseases based on weighted k most similar neighbors","volume":"8","author":"Xuan","year":"2013","journal-title":"PLoS One"},{"key":"2021012203431344100_ref22","doi-asserted-by":"crossref","first-page":"21106","DOI":"10.1038\/srep21106","article-title":"WBSMDA: within and between score for MiRNA-disease association prediction","volume":"6","author":"Chen","year":"2016","journal-title":"Sci Rep"},{"key":"2021012203431344100_ref23","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1186\/1752-0509-7-101","article-title":"Walking the interactome to identify human miRNA-disease associations through the functional link between miRNA targets and disease genes","volume":"7","author":"Shi","year":"2013","journal-title":"BMC Syst Biol"},{"key":"2021012203431344100_ref24","doi-asserted-by":"crossref","first-page":"2792","DOI":"10.1039\/c2mb25180a","article-title":"RWRMDA: predicting novel human microRNA\u2013disease associations","volume":"8","author":"Chen","year":"2012","journal-title":"Mol Biosyst"},{"key":"2021012203431344100_ref25","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1093\/bioinformatics\/btv039","article-title":"Prediction of potential disease-associated microRNAs based on random walk","volume":"31","author":"Xuan","year":"2015","journal-title":"Bioinformatics"},{"key":"2021012203431344100_ref26","doi-asserted-by":"crossref","first-page":"65257","DOI":"10.18632\/oncotarget.11251","article-title":"HGIMDA: heterogeneous graph inference for miRNA-disease association prediction","volume":"7","author":"Chen","year":"2016","journal-title":"Oncotarget"},{"key":"2021012203431344100_ref27","doi-asserted-by":"crossref","first-page":"e1006418","DOI":"10.1371\/journal.pcbi.1006418","article-title":"MDHGI: matrix decomposition and heterogeneous graph inference for miRNA-disease association prediction","volume":"14","author":"Chen","year":"2018","journal-title":"PLoS Comput Biol"},{"key":"2021012203431344100_ref28","doi-asserted-by":"crossref","first-page":"234","DOI":"10.3389\/fgene.2018.00234","article-title":"TLHNMDA: triple layer heterogeneous network based inference for MiRNA-disease association prediction","volume":"9","author":"Chen","year":"2018","journal-title":"Front Genet"},{"key":"2021012203431344100_ref29","doi-asserted-by":"crossref","first-page":"5501","DOI":"10.1038\/srep05501","article-title":"Semi-supervised learning for potential human microRNA-disease associations inference","volume":"4","author":"Chen","year":"2014","journal-title":"Sci Rep"},{"key":"2021012203431344100_ref30","doi-asserted-by":"crossref","first-page":"952","DOI":"10.1080\/15476286.2017.1312226","article-title":"Wu Q-F, Yan G-Y. RKNNMDA: ranking-based KNN for MiRNA-disease association prediction","volume":"14","author":"Chen","year":"2017","journal-title":"RNA Biol"},{"key":"2021012203431344100_ref31","doi-asserted-by":"crossref","first-page":"13877","DOI":"10.1038\/srep13877","article-title":"RBMMMDA: predicting multiple types of disease-microRNA associations","volume":"5","author":"Chen","year":"2015","journal-title":"Sci Rep"},{"key":"2021012203431344100_ref32","doi-asserted-by":"crossref","first-page":"21187","DOI":"10.18632\/oncotarget.15061","article-title":"MCMDA: matrix completion for MiRNA-disease association prediction","volume":"8","author":"Li","year":"2017","journal-title":"Oncotarget"},{"key":"2021012203431344100_ref33","doi-asserted-by":"crossref","first-page":"4256","DOI":"10.1093\/bioinformatics\/bty503","article-title":"Predicting miRNA-disease association based on inductive matrix completion","volume":"34","author":"Chen","year":"2018","journal-title":"Bioinformatics"},{"key":"2021012203431344100_ref34","article-title":"A flexible and robust multi-source learning algorithm for drug repositioning","author":"Chen","year":"2017","journal-title":"In: Acm International Conference on Bioinformatics"},{"key":"2021012203431344100_ref35","doi-asserted-by":"crossref","first-page":"i60","DOI":"10.1093\/bioinformatics\/btu269","article-title":"Inductive matrix completion for predicting gene-disease associations","volume":"30","author":"Natarajan","year":"2014","journal-title":"Bioinformatics"},{"key":"2021012203431344100_ref36","doi-asserted-by":"crossref","first-page":"3357","DOI":"10.1093\/bioinformatics\/bty327","article-title":"Prediction of lncRNA-disease associations based on inductive matrix completion","volume":"34","author":"Lu","year":"2018","journal-title":"Bioinformatics"},{"key":"2021012203431344100_ref37","doi-asserted-by":"crossref","first-page":"3195","DOI":"10.1093\/bioinformatics\/btx390","article-title":"Matrix completion with side information and its applications in predicting the antigenicity of influenza viruses","volume":"33","author":"Huang","year":"2017","journal-title":"Bioinformatics"},{"key":"2021012203431344100_ref38","doi-asserted-by":"crossref","first-page":"D1070","DOI":"10.1093\/nar\/gkt1023","article-title":"Tu J et al. HMDD v2. 0: a database for experimentally supported human microRNA and disease associations","volume":"42","author":"Li","year":"2013","journal-title":"Nucleic Acids Res"},{"key":"2021012203431344100_ref39","doi-asserted-by":"crossref","first-page":"S5","DOI":"10.1186\/1471-2164-11-S4-S5","article-title":"dbDEMC: a database of differentially expressed miRNAs in human cancers","volume":"11","author":"Yang","year":"2010","journal-title":"BMC Genomics"},{"key":"2021012203431344100_ref40","doi-asserted-by":"crossref","first-page":"D98","DOI":"10.1093\/nar\/gkn714","article-title":"miR2Disease: a manually curated database for microRNA deregulation in human disease","volume":"37","author":"Jiang","year":"2009","journal-title":"Nucleic Acids Res"},{"key":"2021012203431344100_ref41","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/S0140-6736(05)17706-X","article-title":"Colorectal cancer","volume":"365","author":"Weitz","year":"2005","journal-title":"Lancet"},{"key":"2021012203431344100_ref42","first-page":"7","volume-title":"Cancer statistics, 2019","author":"Siegel","year":"2019"},{"key":"2021012203431344100_ref43","doi-asserted-by":"crossref","first-page":"27","DOI":"10.3322\/canjclin.44.1.27","article-title":"Colorectal cancer: detection, treatment, and rehabilitation","volume":"44","author":"DeCosse","year":"1994","journal-title":"CA Cancer J Clin"},{"key":"2021012203431344100_ref44","doi-asserted-by":"crossref","first-page":"1901","DOI":"10.7150\/thno.19168","article-title":"Therapeutic inhibition of miR-4260 suppresses colorectal cancer via targeting MCC and SMAD4","volume":"7","author":"Xiao","year":"2017","journal-title":"Theranostics"},{"key":"2021012203431344100_ref45","doi-asserted-by":"crossref","first-page":"1432","DOI":"10.1159\/000490834","article-title":"Knockdown of MiR-20a enhances sensitivity of colorectal cancer cells to Cisplatin by increasing ASK1 expression","volume":"47","author":"Zhang","year":"2018","journal-title":"Cell Physiol Biochem"},{"key":"2021012203431344100_ref46","doi-asserted-by":"crossref","first-page":"6689","DOI":"10.1111\/j.1742-4658.2009.07383.x","article-title":"MicroRNA-143 reduces viability and increases sensitivity to 5-fluorouracil in HCT116 human colorectal cancer cells","volume":"276","author":"Borralho","year":"2009","journal-title":"FEBS J"},{"key":"2021012203431344100_ref47","first-page":"22","article-title":"Esophageal cancer","volume":"95","author":"Short","year":"2017","journal-title":"Am Fam Physician"},{"key":"2021012203431344100_ref48","doi-asserted-by":"crossref","first-page":"2147","DOI":"10.1093\/carcin\/bgs259","article-title":"MiR-196a binding-site SNP regulates RAP1A expression contributing to esophageal squamous cell carcinoma risk and metastasis","volume":"33","author":"Wang","year":"2012","journal-title":"Carcinogenesis"},{"key":"2021012203431344100_ref49","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1158\/1078-0432.CCR-16-0414","article-title":"Downregulation of MicroRNA-644a promotes Esophageal squamous cell carcinoma aggressiveness and stem cell\u2013like phenotype via Dysregulation of PITX2","volume":"23","author":"Zhang","year":"2017","journal-title":"Clin Cancer Res"},{"key":"2021012203431344100_ref50","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1002\/jso.23064","article-title":"Serum microRNA-21 is a novel biomarker in patients with esophageal squamous cell carcinoma","volume":"106","author":"Kurashige","year":"2012","journal-title":"J Surg Oncol"},{"key":"2021012203431344100_ref51","doi-asserted-by":"crossref","first-page":"1727","DOI":"10.1016\/S0140-6736(05)66546-4","article-title":"Breast cancer","volume":"365","author":"Veronesi","year":"2005","journal-title":"Lancet"},{"key":"2021012203431344100_ref52","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1159\/000350100","article-title":"MicroRNA-124 suppresses breast cancer cell growth and motility by targeting CD151","volume":"31","author":"Han","year":"2013","journal-title":"Cell Physiol Biochem"},{"key":"2021012203431344100_ref53","doi-asserted-by":"crossref","first-page":"4263","DOI":"10.1038\/s41598-017-02800-2","article-title":"miR-15a\/miR-16 down-regulates BMI1, impacting Ub-H2A mediated DNA repair and breast cancer cell sensitivity to doxorubicin","volume":"7","author":"Patel","year":"2017","journal-title":"Sci Rep"},{"key":"2021012203431344100_ref54","doi-asserted-by":"crossref","first-page":"e1992e92","DOI":"10.1001\/jamanetworkopen.2019.9292","article-title":"Association of Germline Variants in natural killer cells with tumor immune microenvironment subtypes, tumor-infiltrating lymphocytes, immunotherapy response, clinical outcomes, and cancer risk","volume":"2","author":"Xu","year":"2019","journal-title":"JAMA Netw Open"},{"key":"2021012203431344100_ref55","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.gpb.2017.02.002","article-title":"Network analysis reveals a Signaling regulatory loop in the PIK3CA-mutated breast cancer predicting survival outcome","volume":"15","author":"McGee","year":"2017","journal-title":"Genomics Proteomics Bioinformatics"},{"key":"2021012203431344100_ref56","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.critrevonc.2016.11.017","article-title":"Emergence of miR-34a in radiation therapy","volume":"109","author":"Lacombe","year":"2017","journal-title":"Crit Rev Oncol Hematol"},{"key":"2021012203431344100_ref57","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.canlet.2010.10.006","article-title":"Dysregulation of microRNA-34a expression causes drug-resistance to 5-FU in human colon cancer DLD-1 cells","volume":"300","author":"Akao","year":"2011","journal-title":"Cancer Lett"},{"key":"2021012203431344100_ref58","doi-asserted-by":"crossref","first-page":"1644","DOI":"10.1093\/bioinformatics\/btq241","article-title":"Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases","volume":"26","author":"Wang","year":"2010","journal-title":"Bioinformatics"},{"key":"2021012203431344100_ref59","doi-asserted-by":"crossref","first-page":"3036","DOI":"10.1093\/bioinformatics\/btr500","article-title":"Gaussian interaction profile kernels for predicting drug-target interaction","volume":"27","author":"van Laarhoven","year":"2011","journal-title":"Bioinformatics"},{"key":"2021012203431344100_ref60","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1093\/bioinformatics\/btw715","article-title":"A novel approach based on KATZ measure to predict associations of human microbiota with non-infectious diseases","volume":"33","author":"Chen","year":"2017","journal-title":"Bioinformatics"},{"key":"2021012203431344100_ref61","first-page":"348","volume-title":"Neighborhood Constraint Matrix Completion for Drug-Target Interaction Prediction. Cham","author":"Fan","year":"2018"},{"key":"2021012203431344100_ref62","doi-asserted-by":"crossref","first-page":"e3420","DOI":"10.1371\/journal.pone.0003420","article-title":"An analysis of human microRNA and disease associations","volume":"3","author":"Lu","year":"2008","journal-title":"PLoS One"},{"key":"2021012203431344100_ref63","first-page":"457","volume-title":"An accelerated gradient method for trace norm minimization. Proceedings of the 26th Annual International Conference on Machine Learning. Montreal, Quebec","author":"Ji","year":"2009"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bib\/article-pdf\/22\/1\/485\/35934790\/bbz159.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/academic.oup.com\/bib\/article-pdf\/22\/1\/485\/35934790\/bbz159.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T21:24:45Z","timestamp":1695417885000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/22\/1\/485\/5685754"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,12]]},"references-count":63,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1,12]]},"published-print":{"date-parts":[[2021,1,18]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbz159","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,1]]},"published":{"date-parts":[[2020,1,12]]}}}