{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T13:33:05Z","timestamp":1783776785242,"version":"3.55.0"},"reference-count":75,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T00:00:00Z","timestamp":1756339200000},"content-version":"vor","delay-in-days":58,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Exploring latent microRNA (miRNA)\u2013disease associations (MDAs) is vital for early screening and treatment. Compared with traditional experiments, computational methods enhance efficiency and lower costs in predicting MDAs. We trained the Accurate Matrix Completion for predicting potential MiRNA\u2013Disease Associations (AMCMDA) model in this work, utilizing truncated nuclear norm minimization to improve the prediction accuracy. In AMCMDA, we begin by constructing a heterogeneous network incorporating both similarity and association information between miRNAs and diseases. Second, an optimization framework is designed to complete the effective approximation of the truncated nuclear norm to complement the missing values of the objective matrix. Finally, we solve this optimization problem via Alternating Direction Method of Multipliers and obtain the final prediction scores. After comparing the AMCMDA model with other models across three validation frameworks and three different datasets, we find that the AMCMDA model demonstrates robust and accurate performance. The model\u2019s excellent performance is also demonstrated by two categories of case studies on three diseases.<\/jats:p>","DOI":"10.1093\/bib\/bbaf444","type":"journal-article","created":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T12:21:50Z","timestamp":1756383710000},"source":"Crossref","is-referenced-by-count":3,"title":["Identifying potential miRNA\u2013disease associations through an accurate matrix completion approach"],"prefix":"10.1093","volume":"26","author":[{"given":"Beier","family":"Li","sequence":"first","affiliation":[{"name":"School of Statistics, Renmin University of China, No. 59 Zhongguancun Street, Haidian District , Beijing 100872 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaiyang","family":"Zhong","sequence":"additional","affiliation":[{"name":"College of Information Science & Electronic Engineering, Zhejiang University , No. 866 Yuhangtang Road, Xihu District, Hangzhou 310058, Zhejiang Province ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammet","family":"Deveci","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Turkish Naval Academy, National Defence University , Rauf Orbay Road, 34942 Tuzla, Istanbul ,","place":["T\u00fcrkiye"]},{"name":"Department of Computer Science and Engineering, College of Informatics, Korea University , 145 Anam-ro, Seongbuk-gu, Seoul 02841 ,","place":["Republic of Korea"]},{"name":"Department of Information Technologies, Western Caspian University , 15 B, Jafar Khandan Street, Baku 1001 ,","place":["Azerbaijan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4858-4012","authenticated-orcid":false,"given":"Yong","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Medical Innovation and Research, Chinese PLA General Hospital , No. 28 Fuxing Road, Haidian District, Beijing 100853 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