AIPred: comprehensive prediction and analysis of non-histone acetylation via protein language model and interpretable machine learning
AIPred provides an accurate, interpretable, and accessible computational framework for predicting non-histone acetylation sites, which is expected to accelerate targeted research on non-histone acetylation-related mechanisms in cellular regulation and disease pathways.
Authors
- Hao Luo (ORCID: https://orcid.org/0000-0003-2714-8817)
- Feng Gao (ORCID: https://orcid.org/0000-0002-9563-3841)
- Yuqing Geng (ORCID: https://orcid.org/0009-0004-7871-7594)
Institutions
- Tianjin University (CN)
- Tianjin Synthetic Material Research Institute (China) (CN)
Publication Details
- Journal
- BMC Biology
- Published
- 2025-10-17
- DOI
- https://doi.org/10.1186/s12915-025-02418-1
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China