{"id":"W4392874546","doi":"10.1093/bib/bbae075","title":"Ovarian cancer is detectable from peripheral blood using machine learning over T-cell receptor repertoires","year":2024,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Immune Cell Function and Interaction","field":"Immunology and Microbiology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; Ariel University; Canadian Institutes of Health Research; Terry Fox Foundation; Israel Cancer Research Fund","keywords":"Repertoire; T-cell receptor; clone (Java method); Immune system; Biology; Peripheral blood; Immunology; Receptor; Gene; T cell; Computational biology; Genetics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009560014,0.0004680344,0.0006388553,0.001030575,0.0002006884,0.0008273649,0.0003207658,0.0004080231,0.0005837061],"category_scores_gemma":[0.002885205,0.0001571267,0.0005582141,0.0007156044,0.000219776,0.0004164097,0.0003096044,0.0006746154,0.0004014118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003200907,"about_ca_system_score_gemma":0.0003262429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001375189,"about_ca_topic_score_gemma":0.001070619,"domain_scores_codex":[0.9994677,0.0001666937,0.00003886898,0.0001905398,0.0000646742,0.00007154025],"domain_scores_gemma":[0.9987148,0.0007519081,0.0002203149,0.0001155162,0.0001439531,0.00005353228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007572082,0.0004067445,0.2134931,0.0002270342,0.0003194341,0.0004236721,0.0001554831,0.1237661,0.03422456,0.0009879455,0.002211935,0.6230268],"study_design_scores_gemma":[0.00002043917,0.0001653198,0.04898399,0.00003788618,0.000081937,0.0003375519,0.00005453873,0.9373471,0.007365039,0.004057751,0.001513165,0.00003529468],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7948158,0.00286757,0.1959259,0.0006245366,0.00009478666,0.0001053588,0.001504291,0.001396739,0.0026649],"genre_scores_gemma":[0.9611729,0.0003911498,0.03661882,0.0001099797,0.00007111367,0.00005743345,0.0010413,0.00002040167,0.0005167806],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001375189,"threshold_uncertainty_score":0.005055845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01102600370068756,"score_gpt":0.2331628716340669,"score_spread":0.2221368679333793,"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."}}