{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:12:38Z","timestamp":1768522358837,"version":"3.49.0"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2021,8,4]],"date-time":"2021-08-04T00:00:00Z","timestamp":1628035200000},"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\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11931008"],"award-info":[{"award-number":["11931008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972399"],"award-info":[{"award-number":["61972399"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,11,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In recent years, increasing microRNA (miRNA)\u2013disease associations were identified through traditionally biological experiments. These associations contribute to revealing molecular mechanism of diseases and preventing and curing diseases. To improve the efficiency of miRNA\u2013disease association discovery, some calculation methods were developed as auxiliary tools for researchers. In the current study, we raised a novel model named Bayesian Ranking for MiRNA\u2013Disease Association prediction (BRMDA) by improving Bayesian Personalized Ranking from three aspects: (i) taking advantage of similarity of diseases and miRNAs; (ii) incorporating miRNA bias for miRNAs associated with different number of diseases; and (iii) implementing neighborhood-based approach for new miRNAs and diseases. For each investigated disease, BRMDA used the set of triples (i.e. disease, labeled miRNA, unlabeled miRNA) that reflected association preference of the disease to miRNAs as training set, which made full use of unknown samples rather than simply considering them as negative samples. To investigate the predictive performance of BRMDA, we employed leave-one-out cross-validation and obtained Area Under the Curve of 0.8697, which outperformed many classical methods. Besides, we further implemented three distinct classes of case studies for three common Neoplasms. As a result, there are 44 (Colon Neoplasms), 49 (Esophageal Neoplasms) and 49 (Lung Neoplasms) among the top 50 predicted miRNAs validated through experiments. In short, BRMDA would be a trustable tool for inferring valuable associations.<\/jats:p>","DOI":"10.1093\/bib\/bbab302","type":"journal-article","created":{"date-parts":[[2021,7,19]],"date-time":"2021-07-19T19:10:49Z","timestamp":1626721849000},"source":"Crossref","is-referenced-by-count":23,"title":["Identification of miRNA\u2013disease associations via multiple information integration with Bayesian ranking"],"prefix":"10.1093","volume":"22","author":[{"given":"Chi-Chi","family":"Zhu","sequence":"first","affiliation":[{"name":"Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6795-4007","authenticated-orcid":false,"given":"Chun-Chun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of 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