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Predicting disease-related miRNAs is beneficial to uncovering new biomarkers for the prevention, detection, prognosis, diagnosis and treatment of complex diseases.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In this study, we propose a multi-view Laplacian regularized deep factorization machine (DeepFM) model, MLRDFM, to predict novel miRNA-disease associations while improving the standard DeepFM. Specifically, MLRDFM improves DeepFM from two aspects: first, MLRDFM takes the relationships among items into consideration by regularizing their embedding features via their similarity-based Laplacians. In this study, miRNA Laplacian regularization integrates four types of miRNA similarity, while disease Laplacian regularization integrates two types of disease similarity. Second, to judiciously train our model, Laplacian eigenmaps are utilized to initialize the weights in the dense embedding layer. The experimental results on the latest HMDD v3.2 dataset show that MLRDFM improves the performance and reduces the overfitting phenomenon of DeepFM. Besides, MLRDFM is greatly superior to the state-of-the-art models in miRNA-disease association prediction in terms of different evaluation metrics with the 5-fold cross-validation. Furthermore, case studies further demonstrate the effectiveness of MLRDFM.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bib\/bbac079","type":"journal-article","created":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T20:09:58Z","timestamp":1646942998000},"source":"Crossref","is-referenced-by-count":34,"title":["MLRDFM: a multi-view Laplacian regularized DeepFM model for predicting miRNA-disease associations"],"prefix":"10.1093","volume":"23","author":[{"given":"Yulian","family":"Ding","sequence":"first","affiliation":[{"name":"Division of Biomedical Engineering, University of Saskatchewan, 57 Campus Drive, S7N 5A9, Saskatchewan, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiujuan","family":"Lei","sequence":"additional","affiliation":[{"name":"School of Computer Science, Shaanxi Normal University, 620 West Chang\u2019an Avenue, 710119, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Liao","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Hainan Normal University, 99 Longkun South Road, 571158, Hainan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fang-Xiang","family":"Wu","sequence":"additional","affiliation":[{"name":"Division of Biomedical Engineering, University of Saskatchewan, 57 Campus Drive, S7N 5A9, Saskatchewan, Canada"},{"name":"Department of Mechanical Engineering and Department of Computer Science, University of Saskatchewan, 57 Campus Drive, S7N5A9, Saskatchewan, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,3,22]]},"reference":[{"issue":"2","key":"2022051813180252400_ref1","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/S0092-8674(04)00045-5","article-title":"Micrornas: genomics, biogenesis, mechanism, and 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