Transformer-based deep learning enables improved B-cell epitope prediction in parasitic pathogens: A proof-of-concept study on Fasciola hepatica
deepBCE-Parasite demonstrates excellent performance in predicting BCEs across diverse parasitic pathogens, offering a valuable tool for advancing the design of epitope-based vaccines, antibodies, and diagnostic applications in parasitology.
Authors
- Chun‐Ren Wang (ORCID: https://orcid.org/0000-0003-2726-2644)
- Sayed Haidar Abbas Raza (ORCID: https://orcid.org/0000-0002-0961-1911)
- Rui-Si Hu (ORCID: https://orcid.org/0000-0003-0587-0542)
- Muhammad Ehsan (ORCID: https://orcid.org/0000-0001-5314-5869)
- Kui Gu (ORCID: https://orcid.org/0009-0003-5437-3371)
Institutions
- Sun Yat-sen University (CN)
- Islamia University of Bahawalpur (PK)
- Sichuan University of Arts and Science (CN)
- Heilongjiang Bayi Agricultural University (CN)
- Chongqing University of Technology (CN)
- University of Veterinary and Animal Sciences (PK)
Publication Details
- Journal
- PLoS neglected tropical diseases
- Published
- 2025-04-29
- DOI
- https://doi.org/10.1371/journal.pntd.0012985
- Citations
- 6
- Primary Topic
- vaccines and immunoinformatics approaches
- Type
- article
- Field-Weighted Citation Impact
- 4.84
Funders
- National Natural Science Foundation of China
- Postdoctoral Research Foundation of China