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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.

SUBMITTER: Dao F 

PROVIDER: S-EPMC12371266 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature

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

Dao Fuying F   Lebeau Benjamin B   Ling Crystal Chia Yin CCY   Yang Mi M   Xie Xueqin X   Fullwood Melissa Jane MJ   Lin Hao H   Lyu Hao H  

iMeta 20250527 4


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 replica  ...[more]

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