miRcorrNetPro: Unraveling Algorithmic Insights through Cross-Validation in Multi-Omics Integration for Comprehensive Data Analysis
Özet
High throughput -omics technologies facilitate the
investigation of regulatory mechanisms of complex diseases.
Along this line, scientists develop promising tools and methods
to extend our understanding at the molecular and functional
levels. To this end, miRcorrNet tool performs integrative
analysis of microRNA (miRNA) and gene expression profiles via
machine learning (ML) approach to identify significant miRNA
groups and their associated target genes. In this study, we
propose miRcorrNetPro tool, which extends miRcorrNet by
tracking group scoring, ranking and other information through
the cross-validation iterations. Heatmap visualizations enable
deep novel insights into the collective behavior of clusters of
groups in cellular signaling and hence facilitate detection of
potential biomarkers for the disease under investigation.
Although miRcorrNetPro is designed as a generic tool, here we
present our findings and potential miRNA biomarkers for
Breast Cancer (BRCA). The miRcorrNetPro tool and all other
supplementary files are available at https://github.com/MirayUnlu/miRcorrNetPro.
