{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T14:47:52Z","timestamp":1776091672799,"version":"3.50.1"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T00:00:00Z","timestamp":1741305600000},"content-version":"vor","delay-in-days":14,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003653","name":"Korea National Institute of Health","doi-asserted-by":"publisher","award":["2024-ER-0801-01"],"award-info":[{"award-number":["2024-ER-0801-01"]}],"id":[{"id":"10.13039\/501100003653","id-type":"DOI","asserted-by":"publisher"}]},{"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"}]},{"name":"Korea government","award":["RS-2024-00414964"],"award-info":[{"award-number":["RS-2024-00414964"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Correctly identifying epitope-binding T-cell receptors (TCRs) is important to both understand their underlying biological mechanism in association to some phenotype and accordingly develop T-cell mediated immunotherapy treatments. Although the importance of the CDR3 region in TCRs for epitope recognition is well recognized, methods for profiling their interactions in association to a certain disease or phenotype remains less studied. We developed EpicPred to identify phenotype-specific TCR\u2013epitope interactions. EpicPred first predicts and removes unlikely TCR\u2013epitope interactions to reduce false positives using the Open-set Recognition (OSR). Subsequently, multiple instance learning was used to identify TCR\u2013epitope interactions specific to a cancer type or severity levels of COVID-19 infected patients.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>From six public TCR databases, 244\u00a0552 TCR sequences and 105 unique epitopes were used to predict epitope-binding TCRs and to filter out non-epitope-binding TCRs using the OSR method. The predicted interactions were used to further predict the phenotype groups in two cancer and four COVID-19 TCR-seq datasets of both bulk and single-cell resolution. EpicPred outperformed the competing methods in predicting the phenotypes, achieving an average AUROC of 0.80\u2009\u00b1\u20090.07.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The EpicPred Software is available at https:\/\/github.com\/jaeminjj\/EpicPred.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf080","type":"journal-article","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T15:25:54Z","timestamp":1740151554000},"source":"Crossref","is-referenced-by-count":1,"title":["EpicPred: predicting phenotypes driven by epitope-binding TCRs using attention-based multiple instance learning"],"prefix":"10.1093","volume":"41","author":[{"given":"Jaemin","family":"Jeon","sequence":"first","affiliation":[{"name":"Interdisciplinary Program in Bioinformatics, Seoul National University , Seoul 08826,","place":["Republic of Korea"]}]},{"given":"Suwan","family":"Yu","sequence":"additional","affiliation":[{"name":"Interdisciplinary Program in Bioinformatics, Seoul National University , Seoul 08826,","place":["Republic of Korea"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2479-7606","authenticated-orcid":false,"given":"Sangam","family":"Lee","sequence":"additional","affiliation":[{"name":"College of Computing, Yonsei University , Seoul 03722,","place":["Republic of Korea"]}]},{"given":"Sang Cheol","family":"Kim","sequence":"additional","affiliation":[{"name":"Division of Healthcare and Artificial Intelligence, Korea National Institute of Health , Cheongju 28159,","place":["Republic of Korea"]}]},{"given":"Hye-Yeong","family":"Jo","sequence":"additional","affiliation":[{"name":"Division of Healthcare and Artificial Intelligence, Korea National Institute of Health , Cheongju 28159,","place":["Republic of Korea"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0675-4244","authenticated-orcid":false,"given":"Inuk","family":"Jung","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Kyungpook National University , Daegu 41566,","place":["Republic of Korea"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4586-5062","authenticated-orcid":false,"given":"Kwangsoo","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Transdisciplinary Medicine, Seoul National University Hospital , Seoul 03080,","place":["Republic of Korea"]},{"name":"Department of Medicine, Seoul National University , Seoul 03080,","place":["Republic of Korea"]}]}],"member":"286","published-online":{"date-parts":[[2025,2,21]]},"reference":[{"key":"2025030711570340700_btaf080-B1","doi-asserted-by":"crossref","first-page":"D711","DOI":"10.1093\/nar\/gky964","article-title":"Arrayexpress update\u2014from bulk to single-cell expression 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