miRModuleNet: Detecting miRNA-mRNA Regulatory Modules - Yousef, Malik; Goy, Gokhan; Bakir-Gungor, Burcu (Frontiers in Genetics, 2022)
MicroRNAs have been demonstrated to have a key role in carcinogenesis in a growing number of studies, therefore understanding the regulation mechanisms of miRNAs in gene-regulatory networks is crucial. Understanding the interactions of miRNA and mRNA will help to elucidate the complex biological processes that occur during malignancy. This fact has led to the development of different tools that are able to detect those interactions. Recently we have suggested a machine learning approach, miRCorrnet, based on grouping and scoring (ranking) groups of genes, where each group is associated with a miRNA and its members are the genes that correlate with this specific miRNA. miRCorrnet tool requires two types of -omics data, i.e. miRNA and mRNA expression profiles as an input file. In this study we proposed miRModuleNet, which groups mRNA (genes) for each miRNA to form a star shape, which is also known as the miRNA-mRNA regulatory module. Then the scoring procedure is applied on each module to score those modules in terms of classification. One of the important outputs of miRModuleNet is the list of significant miRNA-mRNA regulatory modules. The tool was validated on external datasets and its output was validated against known databases; additionally functional enrichment analysis was performed. miRModuleNet could aid in the identification of functional relationships between these biomarkers, revealing essential pathways involved in cancer pathogenesis. The miRModuleNet tool and all other supplementary files are available at https://github.com/malikyousef/miRModuleNet/