Identification of MicroRNA Regulatory Modules by Clustering MicroRNA-Target Interactions

Abstract

Identification of microRNA regulatory modules can help decipher microRNA synergistic regulatory mechanism in the development and progression of complex diseases, especially cancers. Experimentally validated microRNA-target interactions provide strong direct evidence for the analysis of microRNA regulatory functions. We here developed a novel computational framework named CMIN to identify microRNA regulatory modules by performing link clustering on such experimentally verified microRNA-target interactions. CMIN runs in two main steps: it first utilizes convolutional autoencoders to extract high-level microRNA-target interaction features from the expression profile data, and then applied affinity propagation clustering algorithm to interaction feature to obtain overlapping microRNA-target clusters. Clusters with significant synergy correlations are considered as microRNA regulatory modules. We tested the proposed framework and other three existing methods on three types of cancer data sets from TCGA (The Cancer Genome Atlas). The results showed that the microRNA regulatory modules detected by CMIN exhibit stronger topological correlation and more functional enrichment compared with other methods. Availability: The supplementary files of CMIN are available at https://github.com/snryou/CMIN.

Keywords

• MicroRNA regulatory module
• MicroRNA-target interaction
• convolutional autoencoder
• affinity propagation
• link clustering