{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T06:06:16Z","timestamp":1784786776994,"version":"3.55.0"},"reference-count":55,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T00:00:00Z","timestamp":1784764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      V gene segments of the T cell receptor (TCR)\n                      <jats:italic>\u03b2<\/jats:italic>\n                      chain (TRBV) are crucial for antigen recognition, yet previous studies have treated them as fixed categories and neglected repertoire-specific immune dynamics that inform functional diversity.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We introduce ClareV, a contrastive learning framework that partitions T cell receptor repertoires into V gene-specific groups to extract data-driven embeddings capturing these dynamics. We evaluated ClareV across three TCR repertoire cohorts spanning cytomegalovirus (CMV), gastric cancer, and multi-cancer settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In the Emerson2017 CMV cohort, the ClareV Random Forest (RF) Fusion model improved AUC by 14.3% over the strongest V-family usage-only baseline (linear SVM) and by 13.3% over the strongest V-gene usage-only baseline (Random Forest). These results indicate that ClareV embeddings capture additional repertoire-level information beyond V-frequency summaries. Ablation studies showed that frequency-weighted bagging and contrastive V-bag learning both contributed to performance, outperforming simple pooling and non-contrastive learned-bag controls. In the CMV cohort, downstream embedding analyses showed that ClareV representations partially recapitulated IMGT-defined V-gene structure while retaining a CMV-responsive, non-sequence component that was largely orthogonal to classical V-usage signals.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>These findings support adaptive V-gene representations as a compact feature space that complements V-frequency summaries for repertoire-level classification.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1866480","type":"journal-article","created":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T05:39:26Z","timestamp":1784785166000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["ClareV: a contrastive learning framework for context-aware TRBV representations in TCR 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China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuepeng","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong","place":["Hong Kong, Hong Kong SAR, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,23]]},"reference":[{"key":"B1","volume-title":"Immunobiology: The Immune System in Health and Disease","author":"Janeway","year":"1997"},{"key":"B2","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1159\/000019123","article-title":"The human t cell receptor beta variable (trbv) genes","volume":"17","author":"Folch","year":"2000","journal-title":"Exp Clin Immunogenetics"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0913939107","article-title":"High throughput sequencing reveals a complex pattern of dynamic interrelationships among human t cell 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