Predicting miRNA-disease association through combining miRNA function and network topological similarities based on MINE.

ABSTRACT: 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. 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 achiev

SUBMITTER: Cao B 

PROVIDER: S-EPMC9618768 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature