EGCPPIS: learning hierarchical equivariant graph representations with contrastive integration for protein–protein interaction site identification
Comprehensive evaluation results on multiple datasets demonstrate that EGCPPIS significantly outperforms state-of-the-art methods. Extensive comparative experiments and case studies further confirm that EGCPPIS can reveal the decision-making patterns in PPIs prediction, facilitating the discovery of potential PPIs. The original datasets and code of EGCPPIS are available at https://github.com/GuicongSun/EGCPPIS .
Authors
- Guicong Sun (ORCID: https://orcid.org/0000-0002-4597-0268)
- Mengxin Zheng (ORCID: https://orcid.org/0009-0008-3777-2641)
- Yongxian Fan (ORCID: https://orcid.org/0000-0003-0120-8092)
- Yangfeng Zhu (ORCID: https://orcid.org/0009-0003-7125-4067)
Institutions
- Guilin University of Electronic Technology (CN)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2025-11-23
- DOI
- https://doi.org/10.1186/s12859-025-06328-5
- Primary Topic
- Bioinformatics and Genomic Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.00