Prediction and analysis of anti-aging peptides using data augmentation and machine learning algorithms
Our study presents three high-performance models for AAP prediction, accelerating the discovery of novel anti-aging peptides. These models not only provide a valuable resource for researchers but also offer insights into the functional mechanisms of AAPs, paving the way for targeted drug development in aging-related therapeutics.
Authors
- Yuanyuan Chen (ORCID: https://orcid.org/0009-0006-5050-5324)
- G. Chen
- Mengen Wang (ORCID: https://orcid.org/0000-0003-4575-9080)
- Yuanyuan Pan (ORCID: https://orcid.org/0000-0002-2891-3448)
- Zhiyuan Zhang
- Erguang Li
- Shihao Wang
Institutions
- Wannan Medical College (CN)
- Shandong University (CN)
- Central China Normal University (CN)
- Honghu Hospital of Traditional Chinese Medicine (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- BMC Biology
- Published
- 2025-12-24
- DOI
- https://doi.org/10.1186/s12915-025-02469-4
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 0.00