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

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 (RWR) 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 HMDD and miR2Disease datasets using leave-one-out cross-validation (LOOCV) and 5-fold cross-validation, MHMDA demonstrates superior performance, achieving AUROC values of 0.938 and 0.913 on HMDD and miR2Disease, respectively, and outperforming existing methods. The integration of multiplex networks enhances prediction accuracy by capturing diverse miRNA functional relationships, which directly contributes to the high AUROC and AUPRC values observed. Additionally, MHMDA’s stability across parameter variations and disease contexts underscores its robustness and potential for real-world applications in identifying novel disease-miRNA associations.