{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001407081,0.0001981977,0.0002210522,0.0001590048,0.0001986537,0.0001294624,0.0001160486,0.0002480788,0.00888263],"category_scores_gemma":[0.00004677641,0.0001835687,0.00009588141,0.0002136225,0.00005713387,0.0004704035,0.00007421823,0.0006361165,0.0003125557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001390874,"about_ca_system_score_gemma":0.00008447418,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01244457,"about_ca_topic_score_gemma":0.00009838775,"domain_scores_codex":[0.9989051,0.00005155048,0.0004821825,0.0001986205,0.00005063117,0.0003119086],"domain_scores_gemma":[0.9995817,0.00007641539,0.000124539,0.0001643236,0.00003558182,0.00001744379],"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":[0.0001740113,0.000095657,0.002943504,0.0001788157,0.000254842,0.000006466204,0.009408762,0.0002306056,0.9530089,0.00006527872,0.02262278,0.01101031],"study_design_scores_gemma":[0.001189628,0.00008943602,0.0001905028,0.0003584595,0.0000823049,0.00004510661,0.0005071886,0.05642292,0.2801302,0.00002084415,0.6606282,0.0003351383],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9726163,0.01479018,0.001432798,0.0005147373,0.005840036,0.0002432858,0.00005971526,0.0003104594,0.004192516],"genre_scores_gemma":[0.9296251,0.002601998,0.006397509,0.005145767,0.0002271986,0.00004274941,0.0005841133,0.0001130193,0.05526258],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6728787,"threshold_uncertainty_score":0.9941316,"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."}}