Combining Gene Expression and Interactions Data with miRNA Family Information for Identifying miRNA-mRNA Regulatory Modules

It is well known that microRNAs (miRNAs) play pivotal roles in gene expression, transcriptional regulation and other important biological processes. An impressive body of literature indicates that miRNAs and mRNAs work cooperatively to form an important part of gene regulatory modules which are extensively involved in cancer. However, with the accumulation of available data, it is a great challenge to identify cancer-related miRNA regulatory modules and uncover their precise regulatory mechanism. This paper proposed a novel computational framework by combining gene expression and interaction data with miRNA family information to identify miRNA-mRNA regulatory modules (GIFMRM), which was evaluated on three heterogeneous datasets. Literature survey, biological significance and functional enrichment analysis were used to validate the obtained results. The analysis results show that the modules identified are highly correlated with the biological conditions in their respective datasets, and they enrich in GO biological processes and KEGG pathways.

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