{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T19:12:34Z","timestamp":1781032354409,"version":"3.54.1"},"reference-count":66,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2022,2,17]],"date-time":"2022-02-17T00:00:00Z","timestamp":1645056000000},"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\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2019ZDPY01"],"award-info":[{"award-number":["2019ZDPY01"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,3,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>In recent years, increasing biological experiments and scientific studies have demonstrated that microRNA (miRNA) plays an important role in the development of human complex diseases. Therefore, discovering miRNA\u2013disease associations can contribute to accurate diagnosis and effective treatment of diseases. Identifying miRNA\u2013disease associations through computational methods based on biological data has been proven to be low-cost and high-efficiency. In this study, we proposed a computational model named Stacked Autoencoder for potential MiRNA\u2013Disease Association prediction (SAEMDA). In SAEMDA, all the miRNA\u2013disease samples were used to pretrain a Stacked Autoencoder (SAE) in an unsupervised manner. Then, the positive samples and the same number of selected negative samples were utilized to fine-tune SAE in a supervised manner after adding an output layer with softmax classifier to the SAE. SAEMDA can make full use of the feature information of all unlabeled miRNA\u2013disease pairs. Therefore, SAEMDA is suitable for our dataset containing small labeled samples and large unlabeled samples. As a result, SAEMDA achieved AUCs of 0.9210 and 0.8343 in global and local leave-one-out cross validation. Besides, SAEMDA obtained an average AUC and standard deviation of 0.9102 \u00b1 \/\u22120.0029 in 100 times of 5-fold cross validation. These results were better than those of previous models. Moreover, we carried out three case studies to further demonstrate the predictive accuracy of SAEMDA. As a result, 82% (breast neoplasms), 100% (lung neoplasms) and 90% (esophageal neoplasms) of the top 50 predicted miRNAs were verified by databases. Thus, SAEMDA could be a useful and reliable model to predict potential miRNA\u2013disease associations.<\/jats:p>","DOI":"10.1093\/bib\/bbac021","type":"journal-article","created":{"date-parts":[[2022,1,24]],"date-time":"2022-01-24T20:11:44Z","timestamp":1643055104000},"source":"Crossref","is-referenced-by-count":65,"title":["Prediction of potential miRNA\u2013disease associations based on stacked autoencoder"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6795-4007","authenticated-orcid":false,"given":"Chun-Chun","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China"},{"name":"Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, 221116, China"}],"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, Xuzhou, 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Huang","sequence":"additional","affiliation":[{"name":"Academy of Arts and Design, Tsinghua University, Beijing, 10084, China"},{"name":"The Future Laboratory, Tsinghua University, Beijing, 10084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9028-5342","authenticated-orcid":false,"given":"Xing","family":"Chen","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,2,17]]},"reference":[{"key":"2022031506313410600_ref1","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1016\/S0092-8674(01)00616-X","article-title":"microRNAs: tiny 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