Prediction of human pathogenic start loss variants based on self-supervised contrastive learning
Collectively, these findings highlight the potential of integrating self-supervised contrastive learning with unlabeled data to mitigate the challenge posed by the scarcity of labeled start loss variants.
Authors
- Junfeng Xia (ORCID: https://orcid.org/0000-0003-3024-1705)
- Yansen Su (ORCID: https://orcid.org/0000-0002-3855-7133)
- Na Cheng (ORCID: https://orcid.org/0009-0000-6123-3842)
- Jie Liu (ORCID: https://orcid.org/0000-0002-1856-8564)
- Henghui Fan (ORCID: https://orcid.org/0000-0002-8150-9954)
Institutions
- Anhui University (CN)
- Anhui Medical University (CN)
Publication Details
- Journal
- BMC Biology
- Published
- 2025-08-08
- DOI
- https://doi.org/10.1186/s12915-025-02348-y
- Primary Topic
- RNA and protein synthesis mechanisms
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China