{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T11:42:57Z","timestamp":1753875777308,"version":"3.41.2"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T00:00:00Z","timestamp":1737504000000},"content-version":"vor","delay-in-days":61,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Institute of Information & Communications Technology Planning & Evaluation"},{"name":"Artificial Intelligence Convergence Innovation Human Resources Development","award":["IITP-2023-00254177"],"award-info":[{"award-number":["IITP-2023-00254177"]}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"MSIT","doi-asserted-by":"publisher","award":["2021R1A2C2010775","2022R1A4A1030189"],"award-info":[{"award-number":["2021R1A2C2010775","2022R1A4A1030189"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Identifying therapeutic genes is crucial for developing treatments targeting genetic causes of diseases, but experimental trials are costly and time-consuming. Although many deep learning approaches aim to identify biomarker genes, predicting therapeutic target genes remains challenging due to the limited number of known targets. To address this, we propose HIT (Hypergraph Interaction Transformer), a deep hypergraph representation learning model that identifies a gene\u2019s therapeutic potential, biomarker status, or lack of association with diseases. HIT uses hypergraph structures of genes, ontologies, diseases, and phenotypes, employing attention-based learning to capture complex relationships. Experiments demonstrate HIT\u2019s state-of-the-art performance, explainability, and ability to identify novel therapeutic targets.<\/jats:p>","DOI":"10.1093\/bib\/bbaf019","type":"journal-article","created":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T14:15:00Z","timestamp":1737555300000},"source":"Crossref","is-referenced-by-count":0,"title":["Therapeutic gene target prediction using novel deep hypergraph representation learning"],"prefix":"10.1093","volume":"26","author":[{"given":"Kibeom","family":"Kim","sequence":"first","affiliation":[{"name":"Division of Artificial Intelligence, Pusan National University , 2 Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan 46241 ,","place":["South Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juseong","family":"Kim","sequence":"additional","affiliation":[{"name":"Division of Artificial Intelligence, Pusan National University , 2 Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan 46241 ,","place":["South 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