Document Type
Article
Publication Date
2025
DOI
10.1002/advs.202508110
Publication Title
Advanced Science
Volume
Advance online publication
Pages
14 pp.
Abstract
The 3D organization of chromatin plays a fundamental role in gene regulation, cellular function, and disease mechanisms. However, current experimental techniques, such as Hi-C, remain costly and labor-intensive, limiting their application in large-scale and disease-related studies. To address this challenge, ChromNet is presented, a multi-task learning framework that integrates epigenetic signals across diverse cell types to enable high-precision prediction of chromatin architecture. By incorporating noise perturbation and auxiliary classification tasks, ChromNet improves the identification of topologically associating domains (TADs) and cell-type-specific chromatin structures, demonstrating superior generalization performance. Notably, ChromNet accurately predicts chromatin interactions in acute myeloid leukemia (AML) samples by leveraging epigenetic signals from both normal and diseased cells, highlighting its potential for studying disease-associated chromatin remodeling. Across multiple key benchmarks, ChromNet consistently outperforms existing models, providing a robust and cost-effective solution for large-scale chromatin conformation studies. This framework enables the exploration of chromatin structural variations across both cell types and disease states, offering new insights into the relationship between 3D genome architecture and gene regulation.
Rights
© 2025 The Authors.
This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Data Availability
Article states: "Data sharing is not applicable to this article as no new data were created or analyzed in this study."
Original Publication Citation
Wang, B., Wang, S., Ding, L., Li, H., Li, Y., & Wang, J. (2025). ChromNet: A multi-task learning framework for cross-cell type prediction of 3D chromatin interactions using epigenetic signals. Advanced Science. Advance online publication. https://doi.org/10.1002/advs.202508110
Repository Citation
Wang, B., Wang, S., Ding, L., Li, H., Li, Y., & Wang, J. (2025). ChromNet: A multi-task learning framework for cross-cell type prediction of 3D chromatin interactions using epigenetic signals. Advanced Science. Advance online publication. https://doi.org/10.1002/advs.202508110
Supporting Information
Included in
Cells Commons, Genetics and Genomics Commons, Medical Genetics Commons, Oncology Commons