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The immense diversity and cross\u2010reactivity of the TCR repertoire makes direct interpretation of antigen specificity from repertoire sequencing challenging. High\u2010throughput sequencing enables large\u2010scale profiling of TCRs but does not directly reveal their target epitopes, requiring computational approaches to bridge this gap. This review outlines two complementary strategies, bottom\u2010up and top\u2010down approaches, to annotate TCR specificity. Bottom\u2010up methods predict TCR\u2010epitope specificity from curated TCR\u2010epitope databases, identifying recurring patterns through distance\u2010based, feature\u2010based, or deep learning models. While effective for well\u2010characterized epitopes, they are limited by biased training data, absence of negative data, and weak generalization to unseen epitopes. Top\u2010down approaches instead infer antigen\u2010driven responses from repertoire\u2010level signals such as sequence similarity, enrichment, and TCR convergence. These methods enable discovery of disease\u2010 or exposure\u2010associated TCR signatures without prior epitope knowledge but are sensitive to technical noise and biological confounding. Both approaches are complementary as bottom\u2010up provides mechanistic specificity, while top\u2010down enables discovery in complex datasets. Their integration, alongside multimodal modeling and improved benchmarking, is key to advancing TCR\u2010epitope annotation and understanding adaptive immune responses.<\/jats:p>","DOI":"10.1111\/imr.70143","type":"journal-article","created":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T03:56:07Z","timestamp":1784519767000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning for TCR Repertoire Epitope Annotation and Pattern Discovery"],"prefix":"10.1111","volume":"340","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2694-5251","authenticated-orcid":false,"given":"Romi","family":"Vandoren","sequence":"first","affiliation":[{"name":"Adrem Data Lab, Department of Computer Science University of Antwerp  Antwerp Belgium"},{"name":"Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS) University of Antwerp  Antwerp Belgium"},{"name":"Antwerp Center for Translational 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