Ge-SAND: an explainable deep learning-driven framework for disease risk prediction by uncovering complex genetic interactions in parallel.

Journal: BMC genomics
PMID: 40312319

Abstract

BACKGROUND: Accurate genetic risk prediction and understanding the mechanisms underlying complex diseases are essential for effective intervention and precision medicine. However, current methods often struggle to capture the intricate and subtle genetic interactions contributing to disease risk. This challenge may be further exacerbated by the curse of dimensionality when considering large-scale pairwise genetic combinations with limited samples. Overcoming these limitations could transform biomedicine by providing deeper insights into disease mechanisms, moving beyond black-box models and single-locus analyses, and enabling a more comprehensive understanding of cross-disease patterns.

Authors

  • Lihang Ye
    Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
  • Liubin Zhang
    Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
  • Bin Tang
    Basic Medical College , Southwest Medical University , Luzhou , Sichuan , China.
  • Junhao Liang
    Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.
  • Ruijie Tan
    Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
  • Hui Jiang
    Queensland Alliance for Environmental Health Science (QAEHS), University of Queensland, 20 Cornwall Street, Woolloongabba, QLD 4012, Australia.
  • Wenjie Peng
    Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
  • Nan Lin
    Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
  • Kun Li
    State Key Laboratory of Veterinary Etiological Biology National Foot-and-Mouth Disease Reference Laboratory Lanzhou Veterinary Research Institute Chinese Academy of Agricultural Sciences, Lanzhou, Gansu, China.
  • Chao Xue
    State Key Laboratory of Tribology in Advanced Equipment, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China.
  • Miaoxin Li
    Center for Precision Medicine and Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.

Keywords

External Resources

Popular Topics
Recent Journals