Potential miRNA-disease association prediction based on kernelized Bayesian matrix factorization.

In this study, we presented a neoteric Bayesian model (KBMFMDA) that combines kernel-based nonlinear dimensionality reduction, matrix factorization and binary classification. The main idea of KBMFMDA is to project miRNAs and disease into a unified subspace and estimating the association network in that subspace. KBMFMDA obtained the AUCs of 0.9132, 0.8708, 0.9008±0.0044 in global and local leave-one-out and five-fold cross validation. Moreover, KBMFMDA was applied to three important human cancers in three different kinds of case studies and most of the top 50 potential disease-related miRNAs were confirmed by many experimental reports. PMID: 31136792 [PubMed - as supplied by publisher]
Source: Genomics - Category: Genetics & Stem Cells Authors: Tags: Genomics Source Type: research