{"id":"W2148211789","doi":"10.1109/ccece.2006.277570","title":"Breast Cancer Prognosis via Gaussian Mixture Regression","year":2006,"lang":"en","type":"article","venue":"","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Cart; Multivariate adaptive regression splines; Regression; Breast cancer; Multivariate statistics; Feature selection; Artificial intelligence; Computer science; Mars Exploration Program; Gaussian process; Regression analysis; Selection (genetic algorithm); Bayesian multivariate linear regression; Pattern recognition (psychology); Gaussian; Cancer; Statistics; Machine learning; Mathematics; Medicine; Internal medicine; Engineering; Biology","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":[0.002863493,0.0004985332,0.0008673861,0.001416619,0.0001600941,0.0007213482,0.0004564381,0.0005858023,0.001002613],"category_scores_gemma":[0.007674157,0.0002565004,0.0006235978,0.000963338,0.0002168006,0.0006544733,0.0005188,0.0006005261,0.0008996067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000374739,"about_ca_system_score_gemma":0.0005583903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005040842,"about_ca_topic_score_gemma":0.003949676,"domain_scores_codex":[0.9990358,0.0005774027,0.00003048439,0.0001146327,0.0001751545,0.00006651801],"domain_scores_gemma":[0.9985305,0.001008117,0.0001313608,0.0001077229,0.0001843434,0.00003779209],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007822635,0.000106289,0.05497558,0.00007055848,0.0003498127,0.0001370162,0.00008634246,0.5072851,0.002601013,0.005002533,0.004745114,0.4238584],"study_design_scores_gemma":[0.0000133337,0.00004448464,0.005268399,0.000008241796,0.00003114233,0.00006388607,0.000007810339,0.9897391,0.0004513568,0.0038157,0.0005415319,0.00001496932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2143612,0.002732962,0.7750512,0.001525418,0.00009419277,0.00006136288,0.000659056,0.003364059,0.002150725],"genre_scores_gemma":[0.9343852,0.0007390068,0.06228983,0.00007670459,0.00008678384,0.00003675128,0.0005949727,0.00007439457,0.001716371],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005040842,"threshold_uncertainty_score":0.01514381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006306415731750771,"score_gpt":0.2500319990329525,"score_spread":0.2437255833012018,"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."}}