{"id":"W4386809570","doi":"10.18280/ria.370411","title":"Anomaly Detection in Human Disease: A Hybrid Approach Using GWO-SVM for Gene Selection","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Support vector machine; Anomaly detection; Gene selection; Vector (molecular biology); Gene; Computer science; Biology; Artificial intelligence; Computational biology; Pattern recognition (psychology); Genetics; Gene expression","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004695808,0.0001393794,0.0001231341,0.0001542098,0.0001897869,0.00003802285,0.0001543735,0.00007711208,0.00001056242],"category_scores_gemma":[0.0002008266,0.0001555158,0.00009245008,0.0003814046,0.00004145195,0.000009572595,0.00006801968,0.0001127642,0.00002038303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004135675,"about_ca_system_score_gemma":0.00003523519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003328138,"about_ca_topic_score_gemma":0.00001688375,"domain_scores_codex":[0.9988996,0.00005200195,0.0003372085,0.0003209039,0.0000872373,0.0003030192],"domain_scores_gemma":[0.999481,0.00002053086,0.0001040728,0.0002597773,0.00006677798,0.0000678662],"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":[0.0000625851,0.00006905301,0.002178407,0.0001510518,0.00001448605,0.000001737083,0.0001325045,0.3946795,0.5974734,0.00007144831,0.0001736379,0.004992166],"study_design_scores_gemma":[0.00004454095,0.00008111778,0.0003172756,0.00001323223,0.00000852919,0.0000121817,0.0000933199,0.6568795,0.3406328,0.00009476089,0.001691008,0.0001317008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5986112,0.00005423182,0.4005098,0.0000203715,0.00008644503,0.0003153683,0.000008787602,0.0000405287,0.0003532088],"genre_scores_gemma":[0.9950294,0.00002629676,0.003604534,0.00003331518,0.000185214,0.00007453596,0.0002182189,0.00002876259,0.0007997436],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3969053,"threshold_uncertainty_score":0.6341749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03286057273742221,"score_gpt":0.301396503875041,"score_spread":0.2685359311376188,"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."}}