摘要：SummaryPredicting 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. In this paper, a new 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 can compete with several state-of-the-art weighted function module algorithms, such as ClusterONE, MCODE, NEMO, and SPICi, and achieved the satisfactory results in terms of the 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 provide clinicians new clues for epidemic diseases, such as COVID-19.Graphical abstractDisplay OmittedHighlights•A new method of ComSim-MINE was proposed.•The harmonic mean between miRNA function and network topology similarities was computed.•The highest composite score in term of F-measure, sensitivity, and accuracy was achieved.•Potential mapping with known associations between disease and miRNA was discussed.Biological sciences; Bioinformatics; Computational bioinformatics;
