{"id":"W2042643721","doi":"10.1002/sim.2254","title":"Application of reliability coefficients in cDNA microarray data analysis","year":2005,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital","funders":"Canadian Institutes of Health Research","keywords":"Normalization (sociology); Computer science; Data mining; Microarray analysis techniques; Gene chip analysis; Reliability (semiconductor); Microarray; Statistics; Computational biology; Bioinformatics; Mathematics; Biology; Gene expression; Gene; Genetics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.03379917,0.001452321,0.001983308,0.00582411,0.0009343678,0.001790835,0.001591177,0.001452191,0.0006541499],"category_scores_gemma":[0.14402,0.000830629,0.001767825,0.004446277,0.001894508,0.001583418,0.001563167,0.00341426,0.0006335564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001340061,"about_ca_system_score_gemma":0.002269876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002162091,"about_ca_topic_score_gemma":0.001479508,"domain_scores_codex":[0.9711336,0.01868384,0.001437703,0.002129075,0.006154516,0.0004611744],"domain_scores_gemma":[0.85209,0.1185439,0.007699459,0.007248247,0.01369626,0.0007221445],"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":[0.0002993625,0.0001369763,0.02325383,0.0006201988,0.0006360889,0.0004688946,0.0007646052,0.5431889,0.01571255,0.07642381,0.003958229,0.3345366],"study_design_scores_gemma":[0.00002907835,0.0001928764,0.005180056,0.00009436277,0.0001043988,0.0004299857,0.0001186499,0.92729,0.009220205,0.05183761,0.005371611,0.0001311958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007713193,0.0005196878,0.9907277,0.0001122171,0.00003889539,0.00008035912,0.00006746915,0.0003140482,0.000426453],"genre_scores_gemma":[0.2294095,0.0009405381,0.7674382,0.0001150686,0.0002022836,0.0005550323,0.0004257101,0.0003524755,0.0005612874],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03379917,"threshold_uncertainty_score":0.1787493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01685903828198683,"score_gpt":0.3394063625897947,"score_spread":0.3225473243078079,"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."}}