A scalable equivariant graph network framework for precise protein function prediction
By offering reliable predictions with biological interpretability, ENGINE contributes to advancing research into cellular processes and disease mechanisms. The model is available at GitHub ( https://github.com/ABILiLab/ENGINE ) and Zenodo ( https://doi.org/10.5281/zenodo.17221153 ), serving as a valuable tool for the broader scientific community.
Authors
- Yue Bi (ORCID: https://orcid.org/0009-0001-6220-7475)
- Jiangning Song (ORCID: https://orcid.org/0000-0001-8031-9086)
- Fuyi Li (ORCID: https://orcid.org/0000-0001-5216-3213)
- Tong Pan (ORCID: https://orcid.org/0009-0003-6676-5727)
- Hao Yi (ORCID: https://orcid.org/0000-0002-3682-5683)
- Zixu Ran (ORCID: https://orcid.org/0009-0005-5831-1607)
- Xudong Guo (ORCID: https://orcid.org/0000-0002-7079-0410)
- Heyun Sun
Institutions
- North West Agriculture and Forestry University (CN)
- Centre for Cancer Biology (AU)
- Monash University (AU)
- Northwest A&F University (CN)
Publication Details
- Journal
- Genome biology
- Published
- 2025-11-29
- DOI
- https://doi.org/10.1186/s13059-025-03886-y
- Citations
- 2
- Primary Topic
- Bioinformatics and Genomic Networks
- Type
- article
- Field-Weighted Citation Impact
- 1.61
Funders
- National Natural Science Foundation of China
- Medical Research Council
- National Health and Medical Research Council
- National Key Research and Development Program of China