{"id":"W2586273138","doi":"10.4236/jdaip.2017.51002","title":"Evaluating Common Strategies for the Efficiency of Feature Selection in the Context of Microarray Analysis","year":2017,"lang":"en","type":"article","venue":"Journal of Data Analysis and Information Processing","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital; University Health Network","funders":"","keywords":"Context (archaeology); Feature selection; Uncorrelated; Null (SQL); Selection (genetic algorithm); Statistics; Null model; Sample size determination; Feature (linguistics); Data mining; Sample (material); Null hypothesis; Computer science; Sensitivity (control systems); Mathematics; Artificial intelligence; Geography; Engineering","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":[0.001428142,0.00004857358,0.0001636232,0.0001809649,0.0002002244,0.0001612744,0.0004220958,0.0000410752,0.000001633428],"category_scores_gemma":[0.0001441662,0.00002626655,0.00008899127,0.0003404346,0.00005431851,0.0002137786,0.000038038,0.0000611715,1.465655e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004170941,"about_ca_system_score_gemma":0.0001004501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002496425,"about_ca_topic_score_gemma":0.0001053968,"domain_scores_codex":[0.9992751,0.00004241428,0.000409541,0.00005983887,0.0001645895,0.00004851966],"domain_scores_gemma":[0.9978718,0.00002516508,0.001488577,0.0002580206,0.0003462459,0.00001024341],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004816166,0.0001205014,0.05147305,0.0002936982,0.002309647,6.904256e-8,0.005769622,0.01257969,0.4859031,0.0001911291,0.001068265,0.4398096],"study_design_scores_gemma":[0.002151119,0.0006135942,0.3049439,0.0001475554,0.008403743,0.00001250821,0.05906052,0.5109374,0.1049654,0.0001262722,0.008379554,0.0002583142],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8089569,0.001169095,0.1890349,0.0006018387,0.00001806901,0.0001038541,0.00003545421,5.164336e-7,0.00007933905],"genre_scores_gemma":[0.9985156,0.000202344,0.00110386,0.0000622059,0.00002358575,0.000001984874,0.0000850811,0.000001094241,0.000004235879],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4983577,"threshold_uncertainty_score":0.1555173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04847456819606222,"score_gpt":0.3819899728301323,"score_spread":0.3335154046340701,"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."}}