{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00368416,0.0004444144,0.0007759072,0.001844775,0.0006616057,0.001277794,0.0006820597,0.001464547,0.002046257],"category_scores_gemma":[0.01649568,0.000322533,0.0005479692,0.00221987,0.002426674,0.001194999,0.001215779,0.001667576,0.0004139632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009919358,"about_ca_system_score_gemma":0.001348261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003690875,"about_ca_topic_score_gemma":0.004589502,"domain_scores_codex":[0.9989737,0.0007060505,0.00002824913,0.00007643415,0.0001750077,0.00004055286],"domain_scores_gemma":[0.9916725,0.007397017,0.0002399285,0.0001846894,0.0003343563,0.0001715263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000747419,0.00006323868,0.005764258,0.0001954405,0.0000665525,0.0002526162,0.0003737637,0.09458157,0.001182009,0.623418,0.005631172,0.2683967],"study_design_scores_gemma":[0.00001381483,0.00001725164,0.001072897,0.00004981321,0.00001093567,0.00009981019,0.00006519788,0.1135935,0.0001771462,0.8754657,0.009412634,0.00002145842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01534265,0.03503165,0.9049782,0.02898464,0.0003438416,0.00004789706,0.0001454979,0.0002536462,0.01487189],"genre_scores_gemma":[0.4081488,0.04106983,0.5358096,0.002878169,0.002378734,0.0002705226,0.000289901,0.00009089107,0.009063547],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003690875,"threshold_uncertainty_score":0.01948392,"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."}}