{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T13:24:34Z","timestamp":1782739474549,"version":"3.54.5"},"reference-count":76,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T00:00:00Z","timestamp":1643587200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,3,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Protein\u2013protein interactions (PPIs) are central to most biological processes. However, reliable identification of PPI sites using conventional experimental methods is slow and expensive. Therefore, great efforts are being put into computational methods to identify PPI sites.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We present Edge Aggregated GRaph Attention NETwork (EGRET), a highly accurate deep learning-based method for PPI site prediction, where we have used an edge aggregated graph attention network to effectively leverage the structural information. We, for the first time, have used transfer learning in PPI site prediction. Our proposed edge aggregated network, together with transfer learning, has achieved notable improvement over the best alternate methods. Furthermore, we systematically investigated EGRET\u2019s network behavior to provide insights about the causes of its decisions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability<\/jats:title>\n                    <jats:p>EGRET is freely available as an open source project at https:\/\/github.com\/Sazan-Mahbub\/EGRET.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Contact<\/jats:title>\n                    <jats:p>shams_bayzid@cse.buet.ac.bd<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bib\/bbab578","type":"journal-article","created":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T07:07:56Z","timestamp":1639724876000},"source":"Crossref","is-referenced-by-count":65,"title":["EGRET: edge aggregated graph attention networks and transfer learning improve protein\u2013protein interaction site prediction"],"prefix":"10.1093","volume":"23","author":[{"given":"Sazan","family":"Mahbub","sequence":"first","affiliation":[{"name":"Department of Computer Science University of Maryland, College Park, Maryland 20742, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md Shamsuzzoha","family":"Bayzid","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering Bangladesh University of Engineering and Technology, Dhaka-1205, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,1,31]]},"reference":[{"issue":"6","key":"2022031506275216500_ref1","article-title":"Protein\u2013protein interactions essentials: key concepts to building and analyzing interactome networks","volume":"6","author":"Rivas","year":"2010","journal-title":"PLoS Comput 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