RepliChrom: Interpretable machine learning predicts cancer‐associated enhancer‐promoter interactions using DNA replication timing
Open accessAbstract
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.
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Traditional ML algorithms have been the cornerstone of early AI-assisted diagnostic models, particularly demonstrating robust performance in scenarios where features are well defined and sample sizes are moderate.
“Traditional ML algorithms have been the cornerstone of early AI-assisted diagnostic models [106–110], particularly demonstrating robust performance in scenarios where features are well defined and sample sizes are moderate.”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [106]
In Results and Discussion (page unavailable), the paper reports that a random-forest model performed best among the algorithms compared, likely because its replication-timing features were relatively simple. This supports robust performance with low-complexity features for enhancer–promoter interaction prediction, but the paper does not establish that traditional ML was a cornerstone of early AI-assisted diagnostic models or that its performance depended on moderate sample sizes. (LLM, conf=0.82)
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- Authors
- Dao, F., Lebeau, B., Ling, C. C. Y., Yang, M., Xie, X., Fullwood, M. J., Lin, H., & Lyu, H.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Nanyang Technological University (SG)
- Agency for Science, Technology and Research (SG)
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China
- Corresponding author
- Fullwood, Melissa Jane
- Lin, Hao
- ORCID
- Venue
- iMeta
- Publisher
- Wiley
- Published
- 2025-05-27
- Volume
- 4
- Issue
- 4
- Pages
- e70052-e70052
- ISSN
- 2770-596X (ISSN-L)
- 2770-5986
- PMC
- PMC12371266
- OpenAlex
- W4410788995
- Cited by
- 23
- References
- 20
- Citations by year
- 2026: 16 · 2025: 7
- FWCI
- 6.91
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- iMeta (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DR-NTU (Nanyang Technological University) (publishedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- China Postdoctoral Science Foundation (2023TQ0047, GZC20230380)
- National Natural Science Foundation of China (62172078, 62402089)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Keywords
- enhancer-promoter interactions, DNA replication timing, long-range chromatin interactions, cancer, leukemia, model interpretability, chromatin regulation, cell type specificity
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