ReviewImmunological reviews2026
Machine Learning of Personal Repertoires From Public T Cell Receptors.
Review in Immunological reviews, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The T-cell 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 TCRβ sequences are independently generated by multiple mechanisms: enrichment by thymic positive selection on a largely shared self-peptide-MHC 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-billion-sequence meta-repertoire (7943 samples, 41 studies) that quantify five interrelated properties: the universe is finite, with Chao2-bounded ceilings (i.e., a lower estimate) of ≈1.97 × 10
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