{"id":"W4286626045","doi":"10.1101/2022.07.21.501053","title":"AIFS: A novel perspective, Artificial Intelligence infused wrapper based Feature Selection Algorithm on High Dimensional data analysis","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"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); Artificial intelligence; Data mining; Categorical variable; Lasso (programming language); Selection (genetic algorithm); Set (abstract data type); Machine learning; Data set; Perspective (graphical); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002123056,0.0009109114,0.001178723,0.001659845,0.0004696099,0.001051699,0.001064149,0.0008789976,0.001003143],"category_scores_gemma":[0.004527166,0.0003161568,0.001204528,0.001729192,0.0005753296,0.0008535214,0.0009302554,0.0008078161,0.000396366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004267077,"about_ca_system_score_gemma":0.0008867013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001955253,"about_ca_topic_score_gemma":0.001185728,"domain_scores_codex":[0.9986349,0.0004586877,0.00009132143,0.0002516639,0.0004891504,0.00007432974],"domain_scores_gemma":[0.9981647,0.0009488809,0.0001736891,0.000212096,0.0004335833,0.00006704492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000257984,0.0001696902,0.006410927,0.0002074745,0.000439288,0.000428785,0.0001725152,0.5229496,0.01163679,0.01244403,0.005860934,0.439022],"study_design_scores_gemma":[0.00001381774,0.00005761629,0.0006550479,0.00001004327,0.00002875963,0.00007038168,0.00001180601,0.9907743,0.002047298,0.004985885,0.001335056,0.000009919328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01212947,0.00024418,0.9864956,0.0001418961,0.00003508305,0.00003673021,0.00006410952,0.0005197834,0.0003331738],"genre_scores_gemma":[0.3642222,0.000439734,0.6315913,0.0003087925,0.0002015589,0.0003264518,0.0007383532,0.0001718806,0.001999773],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002123056,"threshold_uncertainty_score":0.01122797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02763972290092364,"score_gpt":0.2714131375203223,"score_spread":0.2437734146193987,"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."}}