{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004001033,0.0001178963,0.00007299988,0.00003040492,0.00007426849,0.00002044707,0.0001136285,0.000137715,0.0002990838],"category_scores_gemma":[0.00000161199,0.00008369296,0.00005772175,0.00009692491,0.00003025431,0.000002760383,0.00003886375,0.00005012626,0.00001371729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001348446,"about_ca_system_score_gemma":0.00004152065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001213762,"about_ca_topic_score_gemma":0.00009699309,"domain_scores_codex":[0.9992737,0.00002630896,0.0001271842,0.0002985192,0.000113635,0.0001606469],"domain_scores_gemma":[0.9995866,0.000001237432,0.00006253053,0.0002320142,0.00006217988,0.00005539875],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003764391,0.00005392368,0.01200231,0.000007114025,0.000006937247,4.778008e-7,0.000003931345,0.000008027807,0.8586155,0.00008788243,0.1129519,0.0162243],"study_design_scores_gemma":[0.000378328,0.00003375438,0.1043727,0.00003033953,0.00001247097,0.00002609596,0.00002665409,0.00005956158,0.7431929,0.0001155678,0.1515339,0.0002178058],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9084054,0.004486443,0.01481909,0.01564637,0.001046708,0.0007678311,0.00009042188,0.0001705887,0.0545671],"genre_scores_gemma":[0.9880875,0.0001236341,0.0003905014,0.0002839499,0.0005054637,0.00008717141,0.0001118345,0.00001639528,0.01039358],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1154227,"threshold_uncertainty_score":0.34129,"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."}}