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

Buwen Cao; Renfa Li; Sainan Xiao; Shuguang Deng; Xiangjun Zhou; Lang Zhou

doi:10.1016/j.isci.2022.105299

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

iScience. 2022 Oct 7;25(11):105299. doi: 10.1016/j.isci.2022.105299. eCollection 2022 Nov 18.

Authors

Buwen Cao^{1

2}, Renfa Li², Sainan Xiao^{1

2}, Shuguang Deng¹, Xiangjun Zhou¹, Lang Zhou¹

Affiliations

¹ College of Information and Electronic Engineering, Hunan City University, Yiyang 413000, China.
² College of Computer Science and Electronic Engineering, Key Laboratory for Embedded and Cyber-Physical Systems, Hunan University, Changsha 410082, China.

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 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.

Keywords: Bioinformatics; Biological sciences; Computational bioinformatics.