Ünlü Yazici, MirayYousef, MalikMarron, J. S.Bakir-Güngör, BurcuYazici, Miray Unlu2025-09-252025-09-2520239798350337488https://doi.org/10.1109/BIBM58861.2023.10385425https://hdl.handle.net/20.500.12573/4988NSFHigh 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. © 2024 Elsevier B.V., All rights reserved.eninfo:eu-repo/semantics/closedAccessBreast CancerMachine LearningMicroRNAsMulti-Omics IntegrationBiomarkersDiseasesGene ExpressionRna'Omics'AlgorithmicsBreast CancerCross ValidationHigh-ThroughputMachine-LearningMicroRNAMulti-Omic IntegrationOmics TechnologiesRegulatory MechanismMachine LearningConference Object10.1109/BIBM58861.2023.103854252-s2.0-85184912468