RepliChrom: Interpretable machine learning predicts cancer-associated enhancer-promoter interactions using DNA replication timing

Abstract

RepliChrom is an interpretable machine learning model that predicts enhancer-promoter interactions using DNA replication timing across multiple cell types. By integrating replication timing with chromatin interaction data from multiple experimental platforms, it accurately distinguishes true interactions and reveals promoter-region signals as key regulatory drivers. Importantly, the RepliChrom uncovers cancer-specific chromatin patterns in leukemia, offering mechanistic insights into how replication timing shapes long-range gene regulation in both normal and diseased genomes.