{"id":"W2160511690","doi":"10.1109/cibcb.2006.330968","title":"Gene Ontology Driven Feature Selection from Microarray Gene Expression Data","year":2006,"lang":"en","type":"article","venue":"","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Feature selection; Computer science; Redundancy (engineering); Discriminative model; Artificial intelligence; Data mining; Semantic similarity; Gene; Minimum redundancy feature selection; Computational biology; Pattern recognition (psychology); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006293474,0.0001544974,0.0001197812,0.00003980092,0.0001043085,0.00002958545,0.0003914773,0.0002869003,0.0001211039],"category_scores_gemma":[0.00001481399,0.0001340332,0.00004283227,0.00008482517,0.00003536228,0.000007713845,0.0001836292,0.00009383145,0.00002020439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001851835,"about_ca_system_score_gemma":0.00006498626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002131809,"about_ca_topic_score_gemma":0.0004136625,"domain_scores_codex":[0.9987667,0.00007253182,0.0001654461,0.0006821251,0.000114741,0.0001984339],"domain_scores_gemma":[0.9989643,0.000005524339,0.00009580648,0.0008096511,0.00006689513,0.00005784713],"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":[0.00004871136,0.00004480342,0.003502405,0.000001652009,0.00001125707,5.953183e-7,0.000005009609,0.00004112618,0.826336,0.000008452614,0.1691056,0.0008944335],"study_design_scores_gemma":[0.0003758418,0.00003453827,0.008551651,0.000004382484,0.00001194202,0.000007922295,0.00001673184,0.0001659706,0.8231286,0.00005540373,0.1675005,0.0001464687],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9271495,0.002184191,0.06504985,0.001083298,0.0005318646,0.0002600426,0.0001816769,0.00007464167,0.003484902],"genre_scores_gemma":[0.9507986,0.0001643579,0.03267745,0.0004036031,0.001072671,0.00002449861,0.007542568,0.00002669898,0.007289604],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0323724,"threshold_uncertainty_score":0.5465717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01488448574228127,"score_gpt":0.2560535490299608,"score_spread":0.2411690632876795,"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."}}