期刊
BRIEFINGS IN BIOINFORMATICS
卷 23, 期 3, 页码 -出版社
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbac079
关键词
miRNA-disease association prediction; deep factorization machine; Laplacian regularization; Laplacian eigenmaps; multi-view similarity
资金
- Natural Science and Engineering Research Council of Canada (NSERC)
- China Scholarship Council (CSC)
- National Natural Science Foundation of China [2020YFB2104400,61428209]
In this study, a novel model MLRDFM is proposed to predict miRNA-disease associations, which improves the performance of DeepFM by considering relationships among items and using Laplacian regularization. Experimental results show that MLRDFM enhances performance and reduces overfitting, outperforming state-of-the-art models in miRNA-disease association prediction.
Motivation MicroRNAs (miRNAs), as critical regulators, are involved in various fundamental and vital biological processes, and their abnormalities are closely related to human diseases. Predicting disease-related miRNAs is beneficial to uncovering new biomarkers for the prevention, detection, prognosis, diagnosis and treatment of complex diseases. Results 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.
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