{"id":"W2109571426","doi":"10.1007/978-3-642-34123-6_4","title":"A Framework of Gene Subset Selection Using Multiobjective Evolutionary Algorithm","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Gene selection; Computer science; Discriminative model; Feature selection; Curse of dimensionality; Minimum redundancy feature selection; Redundancy (engineering); Evolutionary algorithm; Selection (genetic algorithm); Artificial intelligence; Machine learning; Data mining; Algorithm; Gene; Microarray analysis techniques; Gene expression; Biology; Genetics","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":[0.001224843,0.0009653134,0.001598682,0.001180482,0.0006196384,0.001082434,0.002342662,0.00113418,0.001657676],"category_scores_gemma":[0.001115169,0.0004816348,0.001100371,0.001617243,0.0005905604,0.0007485521,0.001323776,0.0008244911,0.0003483918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006029793,"about_ca_system_score_gemma":0.001004908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002418751,"about_ca_topic_score_gemma":0.001768127,"domain_scores_codex":[0.9994681,0.0001956143,0.00002525008,0.00009943645,0.0001652504,0.00004629254],"domain_scores_gemma":[0.999747,0.0001186384,0.00001730478,0.0000233653,0.00007687169,0.00001684547],"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":[0.00003635118,0.00006147254,0.0004117751,0.00009082071,0.00009171871,0.0001056078,0.00007283507,0.8572468,0.004243867,0.02900807,0.001100965,0.1075298],"study_design_scores_gemma":[0.000007746064,0.00002482764,0.000065929,0.000006415505,0.00001324553,0.00002354626,0.000005747044,0.9941645,0.0003031973,0.004719912,0.0006602162,0.000004671013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003477324,0.0003090357,0.9949169,0.00004715915,0.00002341419,0.00002923025,0.00001581801,0.0001083063,0.001072826],"genre_scores_gemma":[0.1260352,0.0006208133,0.869977,0.00009719223,0.00007184435,0.0003519359,0.0001390556,0.00009339686,0.002613558],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002418751,"threshold_uncertainty_score":0.006477654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01838594546268085,"score_gpt":0.2757314698719958,"score_spread":0.2573455244093149,"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."}}