{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T12:24:03Z","timestamp":1787747043091,"version":"build-2784847793"},"reference-count":114,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,7,26]],"date-time":"2026-07-26T00:00:00Z","timestamp":1785024000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,7,26]],"date-time":"2026-07-26T00:00:00Z","timestamp":1785024000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Immunological Reviews"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    The T\u2010cell receptor (TCR) repertoire records an individual's immunological history, but most unique CDR3 sequences in any one person are private and uninformative about anyone else. A small subset, however, recurs predictably across unrelated donors. These public TCR\u03b2 sequences are independently generated by multiple mechanisms: enrichment by thymic positive selection on a largely shared self\u2010peptide\u2013MHC ligandome, further amplified by selection on common foreign antigens and convergent recombination, together, they are what makes a personal repertoire computationally legible: they provide the shared coordinate system on which otherwise incommensurable repertoires can be aligned and compared. This review takes public TCR sequences as its protagonist. We trace the biology of TCR publicity through new measurements on a 1.5\u2010billion\u2010sequence meta\u2010repertoire (7943 samples, 41 studies) that quantify five interrelated properties: the universe is finite, with Chao2\u2010bounded ceilings (i.e., a lower estimate) of \u22481.97\u2009\u00d7\u200910\n                    <jats:sup>9<\/jats:sup>\n                    amino acid and \u22488.25\u2009\u00d7\u200910\n                    <jats:sup>9<\/jats:sup>\n                    nucleotide CDR3\u03b2 sequences of which \u224822.5% and \u224812% have already been observed; the rarefaction curve already bends within [0, 7943] by a factor of \u00d73.3 (amino acid) and \u00d71.6 (nucleotide) relative to a linear\u2010at\u2010initial\u2010rate extrapolation; publicness is a cohort\u2010scale statistic, with the heavy tail of the publicness distribution growing predictably from\n                    <jats:italic>N<\/jats:italic>\n                    \u2009=\u2009200 to\n                    <jats:italic>N<\/jats:italic>\n                    \u2009=\u20097943; super\u2010public sequences are \u22480.014% of unique amino acid CDR3s but carry 8.45% of the total observation mass; and the recapture rate of a CDR3 already observed in another donor reaches 73.3% (amino acid) and 36.2% (nucleotide) at\n                    <jats:italic>N<\/jats:italic>\n                    \u2009\u2248\u20097900. We then trace the family of computational methods that exploit public sequences as features: frequency vectors, sequence\u2010similarity networks, self\u2010supervised transformer embeddings (CVC, Beaker, TCR\u2010BERT, SCEPTR), and our own anchor\u2010based graph neural networks (GraffiTee). We summarize their performance across cancer detection, autoimmunity, infectious disease, immunotherapy monitoring, and immunological aging, alongside the structural confounders (HLA, age, sex, sequencing platform) that bound generalizability. We connect repertoire\u2010level classification to TCR\u2013pMHC binding prediction and antigen identity, and ask what stands between current capabilities and a universal TCR\u2010based diagnostic.\n                  <\/jats:p>","DOI":"10.1111\/imr.70145","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T05:43:10Z","timestamp":1785130990000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning of Personal Repertoires From Public T Cell Receptors"],"prefix":"10.1111","volume":"341","author":[{"given":"Or","family":"Malca","sequence":"first","affiliation":[{"name":"Faculty of Life Sciences Bar\u2010Ilan University  Ramat Gan Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alona","family":"Zilberberg","sequence":"additional","affiliation":[{"name":"Faculty of Life Sciences Bar\u2010Ilan University  Ramat Gan 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