Ge-SAND: an explainable deep learning-driven framework for disease risk prediction by uncovering complex genetic interactions in parallel
Ge-SAND is a novel deep-learning approach designed to address the challenges of capturing large-scale genetic interactions. By integrating disease risk prediction with interpretable insights into genetic mechanisms, Ge-SAND offers a valuable tool for advancing genomic research and precision medicine.
Authors
- Miaoxin Li (ORCID: https://orcid.org/0000-0002-4733-0109)
- Rui Tan (ORCID: https://orcid.org/0000-0002-7795-1141)
- Bin Tang (ORCID: https://orcid.org/0000-0003-2515-6645)
- Junhao Liang (ORCID: https://orcid.org/0009-0008-2009-993X)
- Wenjie Peng (ORCID: https://orcid.org/0009-0009-6493-6127)
- Lihang Ye (ORCID: https://orcid.org/0000-0003-3942-0283)
- Liubin Zhang (ORCID: https://orcid.org/0000-0003-2509-4333)
- Ke Li (ORCID: https://orcid.org/0009-0000-8949-7321)
- Chao Xue (ORCID: https://orcid.org/0000-0002-6528-3975)
- Hui Jiang (ORCID: https://orcid.org/0000-0002-1861-898X)
- Nan Lin (ORCID: https://orcid.org/0009-0001-0397-7365)
Institutions
- Sun Yat-sen University (CN)
- Third Affiliated Hospital of Zhengzhou University (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- BMC Genomics
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1186/s12864-025-11588-9
- Citations
- 1
- Primary Topic
- Genetic Associations and Epidemiology
- Type
- article
- Field-Weighted Citation Impact
- 1.82
Funders
- National Natural Science Foundation of China
- Basic and Applied Basic Research Foundation of Guangdong Province