Identification of functional miRNA regulatory modules and their associations via dynamic miRNA regulatory function

MicroRNAs (miRNAs) are small non-coding RNAs which cause target genes degradation or translational inhibition. Constructing functional miRNAs regulatory module can be a significant step towards the discovery of their regulatory roles in various development programs. In this paper, we present a Correlated Correspondence Regulatory Module model which builds on modified Correlated Topic Model (CTM). We apply the proposed method to the expression profiles of miRNAs and genes on 89 human cancer samples. The approach computationally predicts miRNA-gene interactions according to the negative or positive correlation relationship between miRNA and gene expression data and identifies functional miRNA regulatory modules from which we can infer multiple and dynamic miRNA function according to the known elements, the result shows consistency with published literature and database. Furthermore, a miRNA regulatory network is constructed in order to study the associations among various regulatory modules, we can detect evolution of miRNA function in biological process according these associations, which solve restriction of traditional methods that only focus on static miRNA function in single regulatory module. Online services can be accessed at the website (http://nclab.hit.edu.cn/CCRM).

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