Predicting miRNA-Disease Associations Using Combination of miRNA Function Similarities and Network Topological Similarities Based on Module Identification Algorithm in Networks

Predicting associations between microRNAs (miRNAs) and diseases from the viewpoint of function modules has become increasingly popular. However, existing methods obtained the relations between diseases and miRNAs only through the construction of similarity networks and neglected the complex network characteristic of the function similarity network. In this paper, a novel method named Combining miRNA function similarities and network topology Similarities based on Module Identification in NEtworks (ComSim-MINE) was developed. Combined similarity is calculated from the harmonic mean between miRNA function similarities and network topology similarities. Experimental results showed that ComSim-MINE outperformed several state-of-the-art weighted function module algorithms, such as ClusterONE, MCODE, NEMO, and SPICi, and achieved the highest composite score of F-measure, sensitivity, and accuracy based on the generated miRNA function interaction network. From the analysis of case studies, some new findings obtained from our proposed method provides clinicians new clues for rare diseases, such as COVID-19.