{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T13:35:42Z","timestamp":1781789742644,"version":"3.54.5"},"reference-count":68,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"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":[[2022,1,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>MicroRNAs (miRNAs) play crucial roles in multiple biological processes and human diseases and can be considered as therapeutic targets of small molecules (SMs). Because biological experiments used to verify SM\u2013miRNA associations are time-consuming and expensive, it is urgent to propose new computational models to predict new SM\u2013miRNA associations. Here, we proposed a novel method called Dual-network Collaborative Matrix Factorization (DCMF) for predicting the potential SM\u2013miRNA associations. Firstly, we utilized the Weighted K Nearest Known Neighbors (WKNKN) method to preprocess SM\u2013miRNA association matrix. Then, we constructed matrix factorization model to obtain two feature matrices containing latent features of SM and miRNA, respectively. Finally, the predicted SM\u2013miRNA association score matrix was obtained by calculating the inner product of two feature matrices. The main innovations of this method were that the use of WKNKN method can preprocess the missing values of association matrix and the introduction of dual network can integrate more diverse similarity information into DCMF. For evaluating the validity of DCMF, we implemented four different cross validations (CVs) based on two distinct datasets and two different case studies. Finally, based on dataset 1 (dataset 2), DCMF achieved Area Under receiver operating characteristic Curves (AUC) of 0.9868 (0.8770), 0.9833 (0.8836), 0.8377 (0.7591) and 0.9836\u2009\u00b1\u20090.0030 (0.8632\u2009\u00b1\u20090.0042) in global Leave-One-Out Cross Validation (LOOCV), miRNA-fixed local LOOCV, SM-fixed local LOOCV and 5-fold CV, respectively. For case studies, plenty of predicted associations have been confirmed by published experimental literature. Therefore, DCMF is an effective tool to predict potential SM\u2013miRNA associations.<\/jats:p>","DOI":"10.1093\/bib\/bbab500","type":"journal-article","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T12:21:12Z","timestamp":1635855672000},"source":"Crossref","is-referenced-by-count":37,"title":["Dual-Network Collaborative Matrix Factorization for predicting small molecule-miRNA associations"],"prefix":"10.1093","volume":"23","author":[{"given":"Shu-Hao","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"}]},{"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, Xuzhou, 221116, China"},{"name":"Artificial Intelligence Research Institute, 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"}]},{"given":"Lian-Ying","family":"Miao","sequence":"additional","affiliation":[{"name":"School of Mathematics, China University of Mining and Technology, Xuzhou, 221116, 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":"School of Information and Control Engineering, China 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