{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,14]],"date-time":"2025-12-14T17:05:28Z","timestamp":1765731928288,"version":"3.44.0"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T00:00:00Z","timestamp":1756166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62401486"],"award-info":[{"award-number":["62401486"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fujian Provincial Natural Science Foundation of China","award":["2024J08021"],"award-info":[{"award-number":["2024J08021"]}]},{"name":"Ministry of Education Industry-University Cooperation and Collaborative Education Project of China","award":["241000783131751"],"award-info":[{"award-number":["241000783131751"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Proteins carry out most biological processes via interactions with other proteins, known as protein\u2013protein interactions (PPIs). Accurately predicting PPIs is crucial for understanding protein function, yet existing methods often fall short in capturing their complex and hierarchical nature.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose PF-AGCN, an adaptive graph convolutional network that leverages two distinct graph structures: a function graph representing hierarchical Gene Ontology term relationships and a protein graph modeling direct interactions between proteins. Unlike traditional graph attention networks, PF-AGCN preserves the original biological structures while dynamically learning new relationships, ensuring the retention of essential biological information. Additionally, our framework integrates a protein language model with stacked dilated causal convolutional neural networks, enabling the synergistic fusion of global sequence semantics and local structural patterns. Extensive experiments on a comprehensive protein dataset across three evaluation facets demonstrate PF-AGCN\u2019s superior prediction accuracy.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code is publicly available at https:\/\/github.com\/smyang107\/PFAGCN.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf473","type":"journal-article","created":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T18:20:22Z","timestamp":1756232422000},"source":"Crossref","is-referenced-by-count":1,"title":["PF-AGCN: an adaptive graph convolutional network for protein\u2013protein interaction-based function prediction"],"prefix":"10.1093","volume":"41","author":[{"given":"Shumin","family":"Yang","sequence":"first","affiliation":[{"name":"School of Electronic Science and Engineering, Xiamen University , Fujian 361005,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4813-019X","authenticated-orcid":false,"given":"Yuhan","family":"Su","sequence":"additional","affiliation":[{"name":"School of Electronic Science and Engineering, Xiamen University , Fujian 361005,","place":["China"]}]},{"given":"Yuchen","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Electronic Science and Engineering, Xiamen University , Fujian 361005,","place":["China"]}]},{"given":"Qin","family":"Lin","sequence":"additional","affiliation":[{"name":"First Affiliated Hospital of Xiamen University , Fujian 361000,","place":["China"]}]},{"given":"Zhong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronic Science and Engineering, Xiamen University , Fujian 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