Deep learning-driven TCR$$\beta$$ repertoire analysis enhances diagnosis and enables mining of immunological biomarkers in systemic lupus erythematosus
This study highlights the potential of DeepTAPE as a supportive tool for biomarker discovery and assessing SLE disease activity, which complements traditional diagnostic approaches. By deepening our understanding of the immunological characteristics and mechanisms associated with SLE, this work lays a foundation for advancing targeted therapies and personalized medicine in autoimmune diseases. Consequently, our findings may pave the way for improved patient outcomes and more effective treatment strategies in the management of SLE.
Authors
- Bowen Tan
- Yifei Sheng (ORCID: https://orcid.org/0000-0002-4431-8382)
- Miaozhe Huo (ORCID: https://orcid.org/0000-0002-3891-3812)
- K.K. Li
- Wan Nie (ORCID: https://orcid.org/0000-0002-0590-6608)
- Shuo Yang (ORCID: https://orcid.org/0000-0003-4164-4438)
- Xikang Feng (ORCID: https://orcid.org/0000-0003-4029-3795)
- Zhao Ling (ORCID: https://orcid.org/0000-0003-0512-6369)
- Ziwei Ma (ORCID: https://orcid.org/0000-0001-9833-8568)
- Tongfei Shen
Institutions
- City University of Hong Kong (HK)
- Northwestern Polytechnical University (CN)
Publication Details
- Journal
- BioData Mining
- Published
- 2025-10-31
- DOI
- https://doi.org/10.1186/s13040-025-00490-5
- Primary Topic
- Systemic Lupus Erythematosus Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- City University of Hong Kong