{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T17:37:11Z","timestamp":1790012231224,"version":"4.0.1"},"reference-count":17,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T00:00:00Z","timestamp":1744416000000},"content-version":"vor","delay-in-days":14,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Institute for Information & Communications Technology Planning & Evaluation"},{"name":"Artificial Intelligence Convergence Innovation Human Resources Development"},{"name":"Korea government"},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,3,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Leveraging deep learning for the representation learning of Gene Ontology (GO) and Gene Ontology Annotation (GOA) holds significant promise for enhancing downstream biological tasks such as protein\u2013protein interaction prediction. Prior approaches have predominantly used text- and graph-based methods, embedding GO and GOA in a single geometric space (e.g. Euclidean or hyperbolic). However, since the GO graph exhibits a complex and nonmonotonic hierarchy, single-space embeddings are insufficient to fully capture its structural nuances.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this study, we address this limitation by exploiting geometric interaction to better reflect the intricate hierarchical structure of GO. Our proposed method, Geometry-Aware Knowledge Graph Embeddings for GO and Genes (GeOKG), leverages interactions among various geometric representations during training, thereby modeling the complex hierarchy of GO more effectively. Experiments at the GO level demonstrate the benefits of incorporating these geometric interactions, while gene-level tests reveal that GeOKG outperforms existing methods in protein\u2013protein interaction prediction. These findings highlight the potential of using geometric interaction for embedding heterogeneous biomedical networks.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/github.com\/ukjung21\/GeOKG.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf160","type":"journal-article","created":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T03:26:22Z","timestamp":1744428382000},"source":"Crossref","is-referenced-by-count":5,"title":["GeOKG: geometry-aware knowledge graph embedding for Gene Ontology and genes"],"prefix":"10.1093","volume":"41","author":[{"given":"Chang-Uk","family":"Jeong","sequence":"first","affiliation":[{"name":"Department of Software and Computer Engineering, Ajou University , Suwon, 16499, South","place":["Korea"]},{"name":"Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104,","place":["USA"]},{"name":"Institute for Biomedical Informatics, University of Pennsylvania , Philadelphia, PA 19104,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaesik","family":"Kim","sequence":"additional","affiliation":[{"name":"Institute for Biomedical Informatics, University of Pennsylvania , Philadelphia, PA 19104,","place":["USA"]},{"name":"Department of Bioengineering, University of Pennsylvania , Philadelphia, PA 19104,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4592-9564","authenticated-orcid":false,"given":"Dokyoon","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104,","place":["USA"]},{"name":"Institute for Biomedical Informatics, University of Pennsylvania , 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