{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T12:28:05Z","timestamp":1780316885508,"version":"3.54.1"},"reference-count":56,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"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":["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":[[2021,5,20]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>MicroRNA (miRNA) plays an important role in the occurrence, development, diagnosis and treatment of diseases. More and more researchers begin to pay attention to the relationship between miRNA and disease. Compared with traditional biological experiments, computational method of integrating heterogeneous biological data to predict potential associations can effectively save time and cost. Considering the limitations of the previous computational models, we developed the model of deep-belief network for miRNA-disease association prediction (DBNMDA). We constructed feature vectors to pre-train restricted Boltzmann machines for all miRNA-disease pairs and applied positive samples and the same number of selected negative samples to fine-tune DBN to obtain the final predicted scores. Compared with the previous supervised models that only use pairs with known label for training, DBNMDA innovatively utilizes the information of all miRNA-disease pairs during the pre-training process. This step could reduce the impact of too few known associations on prediction accuracy to some extent. DBNMDA achieves the AUC of 0.9104 based on global leave-one-out cross validation (LOOCV), the AUC of 0.8232 based on local LOOCV and the average AUC of 0.9048\u00a0\u00b1\u00a00.0026 based on 5-fold cross validation. These AUCs are better than other previous models. In addition, three different types of case studies for three diseases were implemented to demonstrate the accuracy of DBNMDA. As a result, 84% (breast neoplasms), 100% (lung neoplasms) and 88% (esophageal neoplasms) of the top 50 predicted miRNAs were verified by recent literature. Therefore, we could conclude that DBNMDA is an effective method to predict potential miRNA-disease associations.<\/jats:p>","DOI":"10.1093\/bib\/bbaa186","type":"journal-article","created":{"date-parts":[[2020,7,22]],"date-time":"2020-07-22T19:16:40Z","timestamp":1595445400000},"source":"Crossref","is-referenced-by-count":159,"title":["Deep-belief network for predicting potential miRNA-disease associations"],"prefix":"10.1093","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9028-5342","authenticated-orcid":false,"given":"Xing","family":"Chen","sequence":"first","affiliation":[{"name":"Artificial Intelligence Research Institute, China University of Mining and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian-Hao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6795-4007","authenticated-orcid":false,"given":"Chun-Chun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chi-Chi","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,9,1]]},"reference":[{"key":"2021052110412336100_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":"2021052110412336100_ref2","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":"2021052110412336100_ref3","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1038\/msb4100089","article-title":"Principles of microRNA regulation of a human cellular signaling network","volume":"2","author":"Cui","year":"2006","journal-title":"Mol Syst Biol"},{"key":"2021052110412336100_ref4","doi-asserted-by":"crossref","first-page":"1288","DOI":"10.1126\/science.1121566","article-title":"Developmental biology. 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