{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001330619,0.0008826501,0.001512739,0.002247088,0.0003274207,0.001143042,0.001076011,0.001138252,0.0007812295],"category_scores_gemma":[0.001732103,0.0002884224,0.001185768,0.001508992,0.0003652523,0.0006547609,0.0008931664,0.0006821501,0.0003156779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004297708,"about_ca_system_score_gemma":0.0007252123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001659686,"about_ca_topic_score_gemma":0.001826753,"domain_scores_codex":[0.9992864,0.0001790783,0.00004912674,0.0001911571,0.0002093181,0.00008500872],"domain_scores_gemma":[0.999416,0.0002462896,0.00007013184,0.00005599461,0.0001567956,0.00005472374],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000713723,0.0005821389,0.02979016,0.0002926573,0.0006837008,0.0005901555,0.0002233177,0.2502167,0.04667155,0.005589629,0.004101437,0.6605448],"study_design_scores_gemma":[0.00001814388,0.00009865963,0.002266079,0.000008507785,0.00004316125,0.0001357345,0.00002418812,0.9921652,0.002200676,0.002202553,0.000823397,0.00001372663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07432609,0.0007267582,0.9217745,0.0004384911,0.0000882605,0.0001194839,0.0002495236,0.001191215,0.001085723],"genre_scores_gemma":[0.6717086,0.0003250781,0.3240882,0.0003656415,0.0001206575,0.0002350748,0.0007843876,0.0001073943,0.00226488],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002247088,"threshold_uncertainty_score":0.007037044,"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."}}