A New Method to Identify Affected Pathway Subnetworks and Clusters in Colon Cancer

Goy, Gokhan; Yazici, Miray Unlu; Bakir-Gungor, Burcu

doi:10.1109/ubmk.2019.8907141

Cited by 1 publication

(1 citation statement)

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“…Using DAVID, we found that 55 pathways were commonly enriched in all the cancers tested. For these 55 pathways, a pathway - pathway interaction network was generated using the method that was developed in ( Goy et al, 2019 ). A pathway network was obtained by examining the commonalities among the genes of the overrepresented pathways.…”

Section: Resultsmentioning

confidence: 99%

miRModuleNet: Detecting miRNA-mRNA Regulatory Modules

2022

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Increasing evidence that microRNAs (miRNAs) play a key role in carcinogenesis has revealed the need for elucidating the mechanisms of miRNA regulation and the roles of miRNAs in gene-regulatory networks. A better understanding of the interactions between miRNAs and their mRNA targets will provide a better understanding of the complex biological processes that occur during carcinogenesis. Increased efforts to reveal these interactions have led to the development of a variety of tools to detect and understand these interactions. We have recently described a machine learning approach miRcorrNet, based on grouping and scoring (ranking) groups of genes, where each group is associated with a miRNA and the group members are genes with expression patterns that are correlated with this specific miRNA. The miRcorrNet tool requires two types of -omics data, miRNA and mRNA expression profiles, as an input file. In this study we describe miRModuleNet, which groups mRNA (genes) that are correlated with each miRNA to form a star shape, which we identify as a miRNA-mRNA regulatory module. A scoring procedure is then applied to each module to further assess their contribution in terms of classification. An important output of miRModuleNet is that it provides a hierarchical list of significant miRNA-mRNA regulatory modules. miRModuleNet was further validated on external datasets for their disease associations, and functional enrichment analysis was also performed. The application of miRModuleNet aids the identification of functional relationships between significant biomarkers and reveals essential pathways involved in cancer pathogenesis. The miRModuleNet tool and all other supplementary files are available at https://github.com/malikyousef/miRModuleNet/

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Section: Resultsmentioning

confidence: 99%