DconnLoop: a deep learning model for predicting chromatin loops based on multi-source data integration.

Journal: BMC bioinformatics
PMID: 40170155

Abstract

BACKGROUND: Chromatin loops are critical for the three-dimensional organization of the genome and gene regulation. Accurate identification of chromatin loops is essential for understanding the regulatory mechanisms in disease. However, current mainstream detection methods rely primarily on single-source data, such as Hi-C, which limits these methods' ability to capture the diverse features of chromatin loop structures. In contrast, multi-source data integration and deep learning approaches, though not yet widely applied, hold significant potential.

Authors

  • Junfeng Wang
    Department of Colorectal Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Tianjin, China.
  • Kuikui Cheng
    School of Physics and Electronic Information Engineering, Henan Polytechnic University, Jiaozuo, 454003, China.
  • Chaokun Yan
    School of Computer Science and Information Engineering, Henan University, Kaifeng, 475001, China.
  • Huimin Luo
  • Junwei Luo
    College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454003, China.

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