{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Han Y"],"funding":["National Natural Science Foundation of China"],"pagination":["giaf114"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12486388"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["14"],"pubmed_abstract":["<h4>Background</h4>Protein-protein interactions (PPIs) play a crucial role in numerous biological processes. Accurate identification of protein-protein interaction sites is critical for a comprehensive understanding of protein functions and pathological mechanisms. However, conventional experimental approaches for detecting PPIs are often time-consuming and labor-intensive, thereby motivating the development of efficient computational methods to identify PPI sites.<h4>Results</h4>In this work, we propose a novel graph neural network-based method (called MGMA-PPIS) to predict PPI sites by adopting multiview graph embedding and multiscale attention fusion. MGMA-PPIS integrates global node features extracted by an equivariant graph neural network and multiscale local node features extracted b"],"journal":["GigaScience"],"pubmed_title":["MGMA-PPIS: Predicting the protein-protein interaction site with multiview graph embedding and multiscale attention fusion."],"pmcid":["PMC12486388"],"funding_grant_id":["62,173,271","62,473,312"],"pubmed_authors":["Zhang QQ","Han Y","Shi MH","Zhang SW"],"additional_accession":[]},"is_claimable":false,"name":"MGMA-PPIS: Predicting the protein-protein interaction site with multiview graph embedding and multiscale attention fusion.","description":"<h4>Background</h4>Protein-protein interactions (PPIs) play a crucial role in numerous biological processes. Accurate identification of protein-protein interaction sites is critical for a comprehensive understanding of protein functions and pathological mechanisms. However, conventional experimental approaches for detecting PPIs are often time-consuming and labor-intensive, thereby motivating the development of efficient computational methods to identify PPI sites.<h4>Results</h4>In this work, we propose a novel graph neural network-based method (called MGMA-PPIS) to predict PPI sites by adopting multiview graph embedding and multiscale attention fusion. MGMA-PPIS integrates global node features extracted by an equivariant graph neural network and multiscale local node features extracted b","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jan","modification":"2026-06-04T01:47:29.875Z","creation":"2026-05-04T03:13:53.861Z"},"accession":"S-EPMC12486388","cross_references":{"pubmed":["41032018"],"doi":["10.1093/gigascience/giaf114"]}}