{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T04:03:19Z","timestamp":1775102599264,"version":"3.50.1"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001321","name":"National Research Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001321","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ministry of Science & ICT","award":["NRF-2023R1A2C2006953"],"award-info":[{"award-number":["NRF-2023R1A2C2006953"]}]},{"name":"Bio & Medical Technology Development Program","award":["2022M3E5F3085681"],"award-info":[{"award-number":["2022M3E5F3085681"]}]},{"name":"Bio & Medical Technology Development Program","award":["NRF-2022M3E5F3085677"],"award-info":[{"award-number":["NRF-2022M3E5F3085677"]}]},{"name":"Institute of Information & communications Technology Planning & Evaluation","award":["NO.2021\u20130-01343"],"award-info":[{"award-number":["NO.2021\u20130-01343"]}]},{"name":"Artificial Intelligence Graduate School Program"},{"DOI":"10.13039\/501100002551","name":"Seoul National University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002551","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002551","name":"Seoul National University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002551","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>Drug response is conventionally measured at the cell level, often quantified by metrics like IC50. However, to gain a deeper understanding of drug response, cellular outcomes need to be understood in terms of pathway perturbation. This perspective leads us to recognize a challenge posed by the gap between two widely used large-scale databases, LINCS L1000 and GDSC, measuring drug response at different levels\u2014L1000 captures information at the gene expression level, while GDSC operates at the cell line level. Our study aims to bridge this gap by integrating the two databases through transfer learning, focusing on condition-specific perturbations in gene interactions from L1000 to interpret drug response integrating both gene and cell levels in GDSC. This transfer learning strategy involves pretraining on the transcriptomic-level L1000 dataset, with parameter-frozen fine-tuning to cell line-level drug response. Our novel condition-specific gene\u2013gene attention (CSG2A) mechanism dynamically learns gene interactions specific to input conditions, guided by both data and biological network priors. The CSG2A network, equipped with transfer learning strategy, achieves state-of-the-art performance in cell line-level drug response prediction. In two case studies, well-known mechanisms of drugs are well represented in both the learned gene\u2013gene attention and the predicted transcriptomic profiles. This alignment supports the modeling power in terms of interpretability and biological relevance. Furthermore, our model\u2019s unique capacity to capture drug response in terms of both pathway perturbation and cell viability extends predictions to the patient level using TCGA data, demonstrating its expressive power obtained from both gene and cell levels.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code for the CSG2A network is available at https:\/\/github.com\/eugenebang\/CSG2A.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae249","type":"journal-article","created":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T09:23:50Z","timestamp":1719566630000},"page":"i130-i139","source":"Crossref","is-referenced-by-count":8,"title":["Transfer learning of condition-specific perturbation in gene interactions improves drug response prediction"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9217-8380","authenticated-orcid":false,"given":"Dongmin","family":"Bang","sequence":"first","affiliation":[{"name":"Interdisciplinary Program in Bioinformatics, Seoul National University , Seoul, 08826, Republic of Korea"},{"name":"AIGENDRUG Co., Ltd. , Seoul, 08758, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4357-1850","authenticated-orcid":false,"given":"Bonil","family":"Koo","sequence":"additional","affiliation":[{"name":"Interdisciplinary Program in Bioinformatics, Seoul National University , Seoul, 08826, Republic of Korea"},{"name":"AIGENDRUG Co., Ltd. , Seoul, 08758, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5385-9546","authenticated-orcid":false,"given":"Sun","family":"Kim","sequence":"additional","affiliation":[{"name":"Interdisciplinary Program in Bioinformatics, Seoul National University , Seoul, 08826, Republic of Korea"},{"name":"AIGENDRUG Co., Ltd. , Seoul, 08758, Republic of Korea"},{"name":"Department of Computer Science and Engineering, Seoul National University , Seoul, 08826, Republic of Korea"},{"name":"Interdisciplinary Program in Artificial Intelligence, Seoul National University , Seoul, 08826, Republic of Korea"}]}],"member":"286","published-online":{"date-parts":[[2024,6,28]]},"reference":[{"key":"2024071814161898400_btae249-B1","doi-asserted-by":"crossref","first-page":"D679","DOI":"10.1093\/nar\/gkad960","article-title":"Wikipathways 2024: next generation pathway database","volume":"52","author":"Agrawal","year":"2024","journal-title":"Nucleic Acids Res"},{"key":"2024071814161898400_btae249-B2","doi-asserted-by":"crossref","first-page":"18","DOI":"10.3747\/co.v18i1.708","article-title":"Oxaliplatin: a review in the era of molecularly targeted therapy","volume":"18","author":"Alcindor","year":"2011","journal-title":"Curr 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