{"id":"W4389399619","doi":"10.1016/j.cels.2023.11.004","title":"Machine learning analysis of the T cell receptor repertoire identifies sequence features of self-reactivity","year":2023,"lang":"en","type":"article","venue":"Cell Systems","topic":"T-cell and B-cell Immunology","field":"Immunology and Microbiology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Hôpital Maisonneuve-Rosemont; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canada Research Chairs; Exzellenzclusters Entzündungsforschung; Deutsche Forschungsgemeinschaft; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; McGill University","keywords":"T-cell receptor; Repertoire; Major histocompatibility complex; Biology; Reactivity (psychology); Receptor; T cell; Population; Immunology; Computational biology; Genetics; Antigen; Immune system; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005604673,0.0002065706,0.0006617104,0.0002962771,0.0002537826,0.00001395999,0.0004907887,0.0003290374,0.0001252782],"category_scores_gemma":[0.00007055015,0.0001432408,0.0003512749,0.001224916,0.0002746686,0.00006134023,0.000212272,0.0004534525,0.0001842902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004209527,"about_ca_system_score_gemma":0.00005554167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002573342,"about_ca_topic_score_gemma":0.00008714317,"domain_scores_codex":[0.9979513,0.0007688816,0.0005017432,0.0003616752,0.00008202803,0.0003343579],"domain_scores_gemma":[0.9982909,0.0003483127,0.0005994769,0.0006243261,0.0001215361,0.00001546372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003596304,0.0001205807,0.01108919,0.0001969406,0.0007643671,0.000002359495,0.001153355,0.0004586465,0.9842459,0.0000517906,0.001726645,0.0001542578],"study_design_scores_gemma":[0.0004015844,0.00007590613,0.003078675,0.0000455021,0.0005847296,0.000009531922,0.001161187,0.0001463986,0.9737462,0.000004921741,0.02058391,0.0001614255],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9869751,0.007536413,0.000007325411,0.00005106787,0.001665863,0.0002438148,0.00007706162,0.0001456048,0.003297721],"genre_scores_gemma":[0.9490344,0.0004149969,0.000004274137,0.000006753565,0.00001796868,0.00001714328,0.0001572643,0.00001779329,0.05032938],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04703166,"threshold_uncertainty_score":0.5841191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01239370446836775,"score_gpt":0.2207538022506056,"score_spread":0.2083600977822379,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}