{"id":"W2003255402","doi":"10.1177/154407370301700124","title":"Bayesian Machine Learning and Its Potential Applications to the Genomic Study of Oral Oncology","year":2003,"lang":"en","type":"article","venue":"Advances in Dental Research","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Infection and Immunity","funders":"","keywords":"Precision oncology; Bayesian probability; Medicine; Internal medicine; Oncology; Genomic medicine; Clinical Oncology; Computational biology; Computer science; Machine learning; Medical physics; Bioinformatics; Artificial intelligence; Biology; Cancer","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004891932,0.00004840963,0.00006512328,0.00007290813,0.0001109064,0.000008770857,0.000152737,0.00003676474,0.00001731746],"category_scores_gemma":[0.0000697522,0.00003806952,0.00001179623,0.0002010839,0.00004398382,0.000004045917,0.0001218613,0.000142383,0.000004969098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000022404,"about_ca_system_score_gemma":0.00003987667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000162961,"about_ca_topic_score_gemma":0.0005854642,"domain_scores_codex":[0.9990525,0.0003111459,0.0001215156,0.0002175536,0.0001533256,0.0001439352],"domain_scores_gemma":[0.9997168,0.00001770186,0.00002916065,0.0001351116,0.00005469055,0.00004652625],"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":[0.0001724853,0.0003877815,0.04464613,0.00001335976,0.00001204869,0.000002361067,0.0002594791,0.001262277,0.8852724,0.0003317987,0.00005736288,0.0675825],"study_design_scores_gemma":[0.001830125,0.0023608,0.01862594,0.00001138264,0.000008543273,0.00002433762,0.01104572,0.0006196245,0.1366632,0.0002813359,0.8283386,0.0001904208],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916925,0.004949405,0.001140496,0.00008149195,0.00004064693,0.0005881606,0.000002344279,0.000002052943,0.00150291],"genre_scores_gemma":[0.9978449,0.001326981,0.0001349969,0.00001770528,0.00002263134,0.0002123326,0.000006774007,0.00000671589,0.0004269223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8282812,"threshold_uncertainty_score":0.155243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.032605764309448,"score_gpt":0.4248514213565375,"score_spread":0.3922456570470895,"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."}}