An uncertainty-driven gated feature selection network (UGFS-Net) for TG level prediction: linking high-altitude exposure to lipid metabolism disorder via elevated TG
This study developed UGFS-Net, an interpretable deep learning model that accurately predicts triglyceride levels in high-altitude migrants (R² = 0.8776) and provides well-calibrated uncertainty estimates, with identified key biomarkers offering clinical insights.
Authors
- Lei Zhou (ORCID: https://orcid.org/0000-0002-5027-1234)
- Dezhi Sun (ORCID: https://orcid.org/0000-0002-2150-7332)
- Yue Sun
- Ningning Wang (ORCID: https://orcid.org/0000-0002-2820-4094)
- Wei Zhou (ORCID: https://orcid.org/0000-0001-8794-0705)
- Nan Yan (ORCID: https://orcid.org/0000-0003-2232-6813)
- Ziwen Zhou (ORCID: https://orcid.org/0000-0001-5845-6762)
- Caigui Huang
- Guoying Li (ORCID: https://orcid.org/0009-0007-6593-0005)
- Yue Gao
Institutions
- Qinghai University (CN)
- Tianjin University of Traditional Chinese Medicine (CN)
- University of Shanghai for Science and Technology (CN)
- Henan University of Traditional Chinese Medicine (CN)
- Chinese PLA General Hospital (CN)
- Chinese Institute for Brain Research (CN)
- Beijing Radiation Center (CN)
Publication Details
- Journal
- Lipids in Health and Disease
- Published
- 2025-12-10
- DOI
- https://doi.org/10.1186/s12944-025-02826-w
- Primary Topic
- High Altitude and Hypoxia
- Type
- article
- Field-Weighted Citation Impact
- 0.00