{"id":"W4387733089","doi":"10.1186/s12859-023-05502-x","title":"Artificial Intelligence based wrapper for high dimensional feature selection","year":2023,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto; Princess Margaret Cancer Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Prostate Cancer Canada","keywords":"Feature selection; Computer science; Feature (linguistics); Categorical variable; Data mining; Artificial intelligence; Set (abstract data type); Selection (genetic algorithm); Machine learning; Data set; Pattern recognition (psychology)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001590424,0.00010236,0.00007379382,0.0000834184,0.0001197337,0.00002896989,0.0001010104,0.0001509464,0.00001857773],"category_scores_gemma":[0.00007733602,0.00009023729,0.00006695036,0.0002448933,0.00002865169,0.00000589573,0.00003015698,0.00005245378,0.00006018643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001305445,"about_ca_system_score_gemma":0.0001239776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001401203,"about_ca_topic_score_gemma":0.00001116899,"domain_scores_codex":[0.9993368,0.00001435476,0.00019974,0.0001420765,0.0001318838,0.000175123],"domain_scores_gemma":[0.9995683,0.00001763181,0.00008390305,0.0001744434,0.0001042346,0.00005142221],"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":[0.0007948102,0.0001624902,0.00113834,0.0004232297,0.00006210857,3.198393e-7,0.0001830707,0.05406976,0.5204437,0.00819233,0.3137602,0.1007696],"study_design_scores_gemma":[0.0001670486,0.0001935525,0.00111829,0.00001874622,0.00001431948,0.000002129906,0.0001333461,0.5626987,0.3964549,0.0009428029,0.03803417,0.0002219799],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03445533,0.000037446,0.9631736,0.0009067966,0.0005507914,0.0004593591,0.00005816655,0.00009031305,0.0002682091],"genre_scores_gemma":[0.8285808,0.00002779098,0.165242,0.001030118,0.0005751788,0.0002304191,0.002134215,0.00003689227,0.002142573],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7979316,"threshold_uncertainty_score":0.367977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03285917897682682,"score_gpt":0.2858884408104175,"score_spread":0.2530292618335907,"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."}}