{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T12:55:13Z","timestamp":1781873713781,"version":"3.54.5"},"reference-count":208,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T00:00:00Z","timestamp":1781827200000},"content-version":"vor","delay-in-days":49,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"LASIGE Research Unit","award":["UID\/00408\/2025"],"award-info":[{"award-number":["UID\/00408\/2025"]}]},{"name":"European Union\u2019s Horizon 2020 research and innovation programme","award":["101017453"],"award-info":[{"award-number":["101017453"]}]},{"name":"CancerScan project by the EU\u2019s HORIZON Europe research and innovation programme","award":["101186829"],"award-info":[{"award-number":["101186829"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Graph neural networks are a natural fit for protein\u2013protein interaction (PPI) prediction because they exploit the graph structure of interactomes, yet it remains unclear how much they benefit from protein-level representations and which sources of protein information matter most. We address this by surveying and organizing the field into a coherent taxonomy that describes three paradigms: network-level approaches (shallow and deep graph representation learning), protein-level approaches (sequence, structure, and function), and hybrid methods that combine both paradigms\u2014providing a cohesive synthesis that highlights their respective strengths, complementarities, and tradeoffs. We developed a unified framework to systematically compare these paradigms that incorporates multiple representative methods and evaluates them on two widely used benchmarks: OGBl-ppa and HuRI. Our results indicate that hybrid methods have improved performance, with function-driven representations emerging as the most informative protein-level representations for PPI prediction. This work provides a taxonomical map of the field, a reproducible framework for evaluating representative methods across paradigms, and practical guidance for future PPI prediction efforts.<\/jats:p>","DOI":"10.1093\/bib\/bbag313","type":"journal-article","created":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T11:22:04Z","timestamp":1779794524000},"source":"Crossref","is-referenced-by-count":0,"title":["The role of protein embeddings for protein\u2013protein interaction prediction with graph neural networks"],"prefix":"10.1093","volume":"27","author":[{"given":"Laura","family":"Balbi","sequence":"first","affiliation":[{"name":"LASIGE, Faculdade de Ci\u00eancias da Universidade de Lisboa , Campo Grande, 1749-016 Lisbon ,","place":["Portugal"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rita T","family":"Sousa","sequence":"additional","affiliation":[{"name":"University of Mannheim , 68131 Mannheim 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