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Identifying the multiple types of disease-related miRNAs plays an important role in studying the molecular mechanism of miRNAs in diseases. Moreover, compared with traditional biological experiments, computational models are time-saving and cost-minimized. However, most tensor-based computational models still face three main challenges: (i) easy to fall into bad local minima; (ii) preservation of high-order relations; (iii) false-negative samples. To this end, we propose a novel tensor completion framework integrating self-paced learning, hypergraph regularization and adaptive weight tensor into nonnegative tensor factorization, called SPLDHyperAWNTF, for the discovery of potential multiple types of miRNA\u2013disease associations. We first combine self-paced learning with nonnegative tensor factorization to effectively alleviate the model from falling into bad local minima. Then, hypergraphs for miRNAs and diseases are constructed, and hypergraph regularization is used to preserve the high-order complex relations of these hypergraphs. Finally, we innovatively introduce adaptive weight tensor, which can effectively alleviate the impact of false-negative samples on the prediction performance. The average results of 5-fold and 10-fold cross-validation on four datasets show that SPLDHyperAWNTF can achieve better prediction performance than baseline models in terms of Top-1 precision, Top-1 recall and Top-1 F1. Furthermore, we implement case studies to further evaluate the accuracy of SPLDHyperAWNTF. As a result, 98 (MDAv2.0) and 98 (MDAv2.0-2) of top-100 are confirmed by HMDDv3.2 dataset. Moreover, the results of enrichment analysis illustrate that unconfirmed potential associations have biological significance.<\/jats:p>","DOI":"10.1093\/bib\/bbac390","type":"journal-article","created":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T08:50:38Z","timestamp":1664355038000},"source":"Crossref","is-referenced-by-count":10,"title":["Predicting multiple types of miRNA\u2013disease associations using adaptive weighted nonnegative tensor factorization with self-paced learning and hypergraph regularization"],"prefix":"10.1093","volume":"23","author":[{"given":"Dong","family":"Ouyang","sequence":"first","affiliation":[{"name":"Peng Cheng Laboratory , Shenzhen 518055, China"},{"name":"School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology , Avenida Wai Long, Taipa, Macau 999078, China"}]},{"given":"Yong","family":"Liang","sequence":"additional","affiliation":[{"name":"Peng Cheng Laboratory , Shenzhen 518055, China"}]},{"given":"Jianjun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Southwest University , Chongqing 400715, China"}]},{"given":"Xiaoying","family":"Liu","sequence":"additional","affiliation":[{"name":"Computer Engineering Technical College, Guangdong Polytechnic of Science and Technology , Zhuhai 519090, China"}]},{"given":"Shengli","family":"Xie","sequence":"additional","affiliation":[{"name":"Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing , Guangzhou 510000, China"}]},{"given":"Rui","family":"Miao","sequence":"additional","affiliation":[{"name":"Basic Teaching Department, ZhuHai Campus of ZunYi Medical University , Zhuhai 519090, China"}]},{"given":"Ning","family":"Ai","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology , Avenida Wai Long, Taipa, Macau 999078, China"}]},{"given":"Le","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology , Avenida Wai Long, Taipa, Macau 999078, China"}]},{"given":"Qi","family":"Dang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology , Avenida Wai Long, Taipa, Macau 999078, China"}]}],"member":"286","published-online":{"date-parts":[[2022,9,27]]},"reference":[{"key":"2023041000075460600_","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":"2023041000075460600_","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/S0092-8674(04)00045-5","article-title":"Micrornas: genomics, 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