A Novel Cluster-Based Computational Method to Identify miRNA Regulatory Modules.

The identification of miRNA regulatory modules can help decipher miRNAs combinatorial regulation effects on the pathogenesis underlying complex diseases, especially in cancer. By integrating miRNA/mRNA expression profiles and sequence-based predicted target site information, we develop a novel cluster-based computational method named CoModule for identifying miRNA regulatory modules (MRMs). The ultimate goal of CoModule is to detect the MRMs, in which the miRNAs in each module are expected to present cooperative mechanisms in regulating their targets mRNAs.

Identifying Functional Modules in Co-Regulatory Networks Through Overlapping Spectral Clustering.

At the co-regulatory network level, functional modules comprising microRNAs (miRNAs), transcription factors (TFs), and common target genes (mRNAs) are found to be widespread in diverse organisms from bacteria to human, suggesting that co-regulatory functional modules (CRMs) serve as basic building blocks of transcription networks. Identification of CRMs would contribute to explore the miRNAs and TFs regulatory mechanism to specific cancer. However, few studies considered the functional modules which contain TFs and overlapping parts now.

Discovery of microRNAs and Transcription Factors Co-Regulatory Modules by Integrating Multiple Types of Genomic Data.

It is well known that regulators known as microRNA (miRNA) and transcription factor (TF) have been found to play an important role in gene regulation. However, there are few researches of collaborative regulatory (co-regulatory) mechanism between miRNA and TF on system level (function level). Meanwhile, recent advances in high-throughput genomic technologies have enabled researchers to collect diverse large-scale genomic data, which can be used to study the co-regulatory mechanism between miRNA and TF.