{"id":"W4395111832","doi":"10.18280/ria.380206","title":"Evolutionary Hybrid Machine Learning Techniques for DNA Cancer Data Classification","year":2024,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Machine learning; Computer science","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":[0.002548337,0.0005990772,0.0009989395,0.002212196,0.0006387563,0.0009012779,0.001667284,0.001394062,0.00226242],"category_scores_gemma":[0.004456902,0.0004462411,0.001158352,0.001971798,0.0007998747,0.001119211,0.001302659,0.001382998,0.0004433401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007387577,"about_ca_system_score_gemma":0.00050563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002549552,"about_ca_topic_score_gemma":0.002743393,"domain_scores_codex":[0.9987403,0.0005243784,0.0001034544,0.0001859639,0.0003615038,0.00008452279],"domain_scores_gemma":[0.9969314,0.002183561,0.0001037647,0.0002169106,0.0005050643,0.00005922291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002306088,0.0002319929,0.003240587,0.00009903889,0.0002553281,0.0001536,0.0002428539,0.3349362,0.01007784,0.007713477,0.001216509,0.641602],"study_design_scores_gemma":[0.00001703224,0.00006558681,0.0006040493,0.000008208934,0.00002831662,0.00006999901,0.00002702527,0.9929047,0.001989543,0.003695178,0.0005811036,0.00000925814],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08576214,0.0008737937,0.9104698,0.0003058445,0.00009287919,0.00007451851,0.00005662504,0.0005641356,0.001800208],"genre_scores_gemma":[0.4313229,0.0003533391,0.5623347,0.0002972413,0.0001276658,0.0002088868,0.0002636605,0.0001271492,0.004964408],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002549552,"threshold_uncertainty_score":0.01347703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07071135442849887,"score_gpt":0.3434292541124107,"score_spread":0.2727178996839119,"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."}}