{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T19:47:52Z","timestamp":1784317672053,"version":"3.55.0"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"vor","delay-in-days":9,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fresh Wind Biotechnologies USA Inc."}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Accurate prediction of TCR-pMHC binding is important for the development of cancer immunotherapies, especially TCR-based agents. Existing algorithms often experience diminished performance when dealing with unseen epitopes, primarily due to the complexity in TCR-pMHC recognition patterns and the scarcity of available data for training. We have developed a novel deep learning model, \u2018TCR Antigen Binding Recognition\u2019 based on BERT, named as TABR-BERT. Leveraging BERT's potent representation learning capabilities, TABR-BERT effectively captures essential information regarding TCR-pMHC interactions from TCR sequences, antigen epitope sequences and epitope-MHC binding. By transferring this knowledge to predict TCR-pMHC recognition, TABR-BERT demonstrated better results in benchmark tests than existing methods, particularly for unseen epitopes.<\/jats:p>","DOI":"10.1093\/bib\/bbad436","type":"journal-article","created":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T02:38:01Z","timestamp":1701484681000},"source":"Crossref","is-referenced-by-count":28,"title":["Accurate TCR-pMHC interaction prediction using a BERT-based transfer learning method"],"prefix":"10.1093","volume":"25","author":[{"given":"Jiawei","family":"Zhang","sequence":"first","affiliation":[{"name":"Fresh Wind Biotechnologies Inc. (Tianjin) , Tianjin , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wang","family":"Ma","sequence":"additional","affiliation":[{"name":"Fresh Wind Biotechnologies Inc. 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