{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001047155,0.0004003001,0.0003783417,0.001108411,0.0002078694,0.0006508264,0.0003023481,0.0003924931,0.001108698],"category_scores_gemma":[0.002800938,0.0001069808,0.0004345435,0.0006464053,0.0002115317,0.000352429,0.0002281105,0.000520888,0.0004241081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003145231,"about_ca_system_score_gemma":0.0002587469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008597026,"about_ca_topic_score_gemma":0.0009499876,"domain_scores_codex":[0.9996344,0.0001273296,0.00002568723,0.0001098502,0.00005668573,0.00004594864],"domain_scores_gemma":[0.9981297,0.001309115,0.0001772255,0.0001486427,0.0001782469,0.00005721999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009701615,0.0007080201,0.2695923,0.000362874,0.0003594418,0.0003198561,0.0001281266,0.2366149,0.1840433,0.001982755,0.002857414,0.3020608],"study_design_scores_gemma":[0.00001434176,0.0002431139,0.09406304,0.00002362523,0.00004784257,0.0001689768,0.00004646357,0.8745786,0.02597359,0.003674397,0.001144383,0.00002162822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9293971,0.0003575319,0.06587909,0.0001608545,0.00002048585,0.00003868101,0.001913429,0.0004873129,0.001745593],"genre_scores_gemma":[0.9780216,0.0001111535,0.01896986,0.0000378349,0.00001183545,0.00003374135,0.002448191,0.0000207744,0.0003449935],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001108698,"threshold_uncertainty_score":0.005537927,"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."}}