{"id":"W1974529710","doi":"10.1109/iembs.2010.5627300","title":"Fast orthogonal search for genetic feature selection","year":2010,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Feature selection; Multivariate statistics; Artificial intelligence; Selection (genetic algorithm); Pattern recognition (psychology); Data mining; Feature (linguistics); Regression; Scale (ratio); Machine learning; Mathematics; Statistics","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":[0.0001256046,0.00009241402,0.00006029112,0.00002830841,0.00009230067,0.00002894579,0.0001200474,0.000177816,0.00009904589],"category_scores_gemma":[0.0000545411,0.00007962992,0.00005812553,0.00005295055,0.00003281098,0.000001798,0.00004546643,0.0002035492,0.0000183725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003104203,"about_ca_system_score_gemma":0.00005626505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004205174,"about_ca_topic_score_gemma":0.00008262561,"domain_scores_codex":[0.9994587,0.00001248721,0.00009514614,0.0001526078,0.00009035548,0.0001907386],"domain_scores_gemma":[0.9996369,0.000009437344,0.00002876828,0.0001621081,0.0001041168,0.00005865299],"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":[0.00005468896,0.0000285937,0.03246716,0.00003804722,0.00002980357,1.900745e-7,0.00003384275,0.0004660887,0.9376686,0.001083281,0.01369103,0.01443873],"study_design_scores_gemma":[0.0009688429,0.0006798612,0.05299077,0.0000042154,0.00002207486,0.0001522484,0.00005342022,0.0323812,0.5137715,0.00007309236,0.3984725,0.0004303091],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9319882,0.00001544813,0.06029198,0.0003176829,0.0002027232,0.0002354057,0.00001123448,0.00002782357,0.0069095],"genre_scores_gemma":[0.8320187,0.000006989801,0.1571584,0.0003786699,0.0005821302,0.0000258093,0.0001866785,0.00002262913,0.009619983],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4238971,"threshold_uncertainty_score":0.3247214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004912763823578838,"score_gpt":0.2627368011366409,"score_spread":0.257824037313062,"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."}}