miRcorrNetPro: Unraveling Algorithmic Insights through Cross-Validation in Multi-Omics Integration for Comprehensive Data Analysis

Yazici M. U., Yousef M., Marron J., Bakir-Gungor B.

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023, İstanbul, Turkey, 5 - 08 December 2023, pp.3234-3240 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/bibm58861.2023.10385425
  • City: İstanbul
  • Country: Turkey
  • Page Numbers: pp.3234-3240
  • Keywords: Breast Cancer, Machine Learning, microRNAs, Multi-omics Integration
  • Abdullah Gül University Affiliated: Yes

Abstract

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/Miray-Unlu/miRcorrNetPro.