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

Institutions

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
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

EGCPPIS: learning hierarchical equivariant graph representations with contrastive integration for protein–protein interaction site identification

Guicong Sun, Mengxin Zheng, Yongxian Fan, Yangfeng Zhu
BMC Bioinformatics
Bioinformatics and Genomic Networks
article

EGCPPIS: learning hierarchical equivariant graph representations with contrastive integration for protein–protein interaction site identification

Guicong Sun, Mengxin Zheng, Yongxian Fan, Yangfeng Zhu
article en

Abstract

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 .

BMC BioinformaticsVol. 26(1)
Guilin University of Electronic Technology (CN)
Openalex Percentile: Top 68%
Bioinformatics and Genomic Networks
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.