{"id":"W2911228313","doi":"10.1172/jci.insight.123879","title":"Treg gene signatures predict and measure type 1 diabetes trajectory","year":2019,"lang":"en","type":"article","venue":"JCI Insight","topic":"Diabetes and associated disorders","field":"Biochemistry, Genetics and Molecular Biology","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of British Columbia Hospital; University of Toronto; Prevention of Organ Failure; BC Children's Hospital","funders":"National Institutes of Health; Immune Tolerance Network; BC Children's Hospital; Juvenile Diabetes Research Foundation United States of America; National Institute of Allergy and Infectious Diseases; Division of Intramural Research, National Institute of Allergy and Infectious Diseases; Pfizer; Bristol-Myers Squibb","keywords":"Biomarker; Immunotherapy; Type 1 diabetes; Gene signature; Medicine; Diabetes mellitus; Immune system; Type 2 diabetes; Immunology; Gene; Signature (topology); Oncology; Bioinformatics; Computational biology; Internal medicine; Biology; Gene expression; Endocrinology; Genetics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007276056,0.0001321771,0.0001352475,0.0000295123,0.00004057445,0.00002474718,0.0000791971,0.0001954739,0.00006296553],"category_scores_gemma":[0.00004144979,0.0001136149,0.00005707935,0.00007413852,0.00003566859,0.000004485263,0.00004579869,0.00008825863,0.00002173936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006622521,"about_ca_system_score_gemma":0.000039297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000322206,"about_ca_topic_score_gemma":0.00001084734,"domain_scores_codex":[0.9992726,0.00003539211,0.0001011242,0.0002654495,0.0001085155,0.0002168999],"domain_scores_gemma":[0.9996416,0.00001072917,0.00004515645,0.0001856488,0.00005343726,0.00006340351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002963616,0.00007692049,0.04153778,0.00002435693,0.000155974,0.000001289939,0.00006896658,0.00003569021,0.9432874,0.00003522994,0.01158533,0.003161465],"study_design_scores_gemma":[0.002176123,0.001323178,0.07321299,0.00005483245,0.00009589038,0.000001407127,0.0001225653,0.0001704668,0.4576732,0.0001439051,0.4642638,0.0007617083],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9797127,0.01201242,0.000002344273,0.00007162955,0.0002582629,0.0001344978,0.00001637732,0.00001944738,0.007772308],"genre_scores_gemma":[0.9974625,0.0003438156,0.00004717442,0.0004804211,0.0001307414,0.000006148859,0.0001233225,0.00002210313,0.001383774],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4856142,"threshold_uncertainty_score":0.4633083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004202438066666239,"score_gpt":0.1874355535367867,"score_spread":0.1832331154701205,"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."}}