Hessian Regularized $$L_{2,1}$$ -Nonnegative Matrix Factorization and Deep Learning for miRNA –Disease Associations Prediction

AbstractSince the identification of microRNAs (miRNAs), empirical research has demonstrated their crucial involvement in the functioning of organisms. Investigating miRNAs significantly bolsters efforts related to averting, diagnosing, and treating intricate human maladies. Yet, exploring every conceivable miRNA –disease association consumes significant resources and time within conventional wet experiments. On the computational front, forecasting potential miRNA–disease connections serves as a valuable source of preliminary insights for medical investigators. As a result, we have developed a novel matr ix factorization model known as Hessian-regularized\(L_{2,1}\) nonnegative matrix factorization in combination with deep learning for predicting associations between miRNAs and diseases, denoted as\(HRL_{2,1}\)-NMF-DF. In particular, we introduce a novel iterative fusion approach to integrate all similarities. This method effectively diminishes the sparsity of the initial miRNA –disease associations matrix. Additionally, we devise a mixed model framework that utilizes deep learning, matrix decomposition, and singular value decomposition to capture and depict the intricate nonlinear features of miRNA and disease. The prediction performance of the six matrix factorization methods is improved by comparison and analysis, similarity matrix fusion, data preprocessing, and parameter adjustment. The AUC and AUPR obtained by the new matrix factorization model under fivefold cross...
Source: Interdisciplinary Sciences, Computational Life Sciences - Category: Bioinformatics Source Type: research