An Information Entropy-based Method to Detect microRNA Regulatory Module

Abstract

The detection of miRNA regulatory modules (MRMs) can facilitate the analysis of miRNA combination effects in aberrant transcriptional regulatory networks. Existing methods suffer from stochastic or require a predetermined number of regulatory modules. We here develop a parameter-free computational framework named DeMine to predict MRMs. Briefly, DeMine is an information entropy-based method implemented by three steps. It first transforms miRNA regulatory network into a miRNA-miRNA synergistic network, and then detects miRNA clusters by maximizing the referred cluster entropy density. After that, the co-regulated mRNAs are added into corresponding clusters to form final MRMs. Compared with existing methods Mirsynergy and BCM on synthetic dataset and three real cancer datasets: ovarian cancer (OVCA), breast cancer (BRCA) and thyroid cancer (THCA) datasets, the proposed method can not only exhibit higher accuracy but also extract larger cohesiveness-preserved MRMs. More importantly, DeMine can find more miRNAs as tumor markers for the diagnosis of tumors.