{"id":"W2555124762","doi":"10.6000/1929-6029.2017.06.03.3","title":"Using Copulas to Select Prognostic Genes in Melanoma Patients","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"African Union","keywords":"False discovery rate; Copula (linguistics); Resampling; Statistics; Covariate; Mathematics; Gene selection; Parametric statistics; Computer science; Econometrics; Computational biology; Data mining; Microarray analysis techniques; Biology; Gene; Genetics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00873281,0.0008563309,0.001469792,0.001502667,0.0003928824,0.001182574,0.0009161526,0.0007622391,0.001080795],"category_scores_gemma":[0.02526522,0.0005283834,0.001619344,0.001156477,0.0007446592,0.0008037764,0.0008490871,0.001069601,0.0003096611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007341418,"about_ca_system_score_gemma":0.001206184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004328608,"about_ca_topic_score_gemma":0.003216853,"domain_scores_codex":[0.9982691,0.001133426,0.00006495847,0.0002772813,0.0001065597,0.0001486571],"domain_scores_gemma":[0.9846039,0.01252569,0.001108006,0.0008591483,0.0005982221,0.0003050097],"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":[0.00119564,0.0002091349,0.120042,0.0001294917,0.0007599433,0.0009729756,0.0003075002,0.7967812,0.004388364,0.008621508,0.003638831,0.06295338],"study_design_scores_gemma":[0.00003657782,0.00005463812,0.006694684,0.00001058615,0.00006182525,0.00006954515,0.00003922199,0.9865978,0.0005556859,0.005608703,0.0002539088,0.00001685646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5926361,0.00110974,0.4029572,0.001042262,0.00008152359,0.0001378419,0.0006944059,0.0005385948,0.0008023058],"genre_scores_gemma":[0.9686216,0.0003419815,0.02937086,0.0001763773,0.00005798417,0.0001020556,0.0008419097,0.00006799606,0.0004192227],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00873281,"threshold_uncertainty_score":0.04618406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09690763479675663,"score_gpt":0.4889209519417816,"score_spread":0.392013317145025,"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."}}