2019 Volume 12 Pages 1-8
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.