Integrating multiple microRNA functional similarity networks for improved disease-microRNA association prediction.

ABSTRACT: MicroRNAs (miRNAs) play a critical role in disease mechanisms, making the identification of disease-associated miRNAs essential for precision medicine. We propose a novel computational method, multiplex-heterogeneous network for MiRNA-disease associations (MHMDA), which integrates multiple miRNA functional similarity networks and a disease similarity network into a multiplex-heterogeneous network. This approach employs a tailored random walk with restart algorithm to predict disease-miRNA associations, leveraging the complementary information from experimentally validated and predicted miRNA-target interactions, as well as disease phenotypic similarities. Evaluated on the human microRNA disease database and miR2Disease datasets using leave-one-out cross-validation and 5-fold cross-validati

SUBMITTER: Le DH 

PROVIDER: S-EPMC12410926 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature