Uncovering co-regulatory modules and gene regulatory networks in the heart through machine learning-based analysis of large-scale epigenomic data

Naima Vahab, Tarun Bonu, Levin Kuhlmann, Mirana Ramialison, Sonika Tyagi

Abstract: Highlights•A novel predictive pipeline for identifying putative CRMs from DNA motifs, employing a combination of RFC and CNN machine learning models.•Our work identifies crucial factors in predicting TFBS co-operativity, contributing to a deeper understanding of transcription factor interactions.•The versatility of our pipeline is highlighted by its successful application in identifying novel cardiac CRMs and gene regulatory networks associated with cardiac development and disease pathways.•The adaptability of the pipeline extends its utility to diverse biological systems and datasets beyond the scope of cardiac pathways studied in this work.•The tool is available both as an open source code and via web server.