{"id":"W4297002497","doi":"10.3389/fgene.2022.992070","title":"Deep learning methods may not outperform other machine learning methods on analyzing genomic studies","year":2022,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; University of Victoria","funders":"National Key Research and Development Program of China; Western Canada Research Grid; Medical Research Council; Compute Canada","keywords":"Deep learning; Artificial intelligence; Computer science; Machine learning; Biobank; Support vector machine; Benchmark (surveying); Elastic net regularization; Personalized medicine; Big data; Sample (material); F1 score; Sample size determination; Bioinformatics; Data mining; Biology; Statistics; Mathematics; Feature selection","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":[0.02835275,0.001730358,0.001256676,0.002871519,0.0008385581,0.002571862,0.001525316,0.00211437,0.001947697],"category_scores_gemma":[0.0785896,0.0004667499,0.001140199,0.002700289,0.001321479,0.005211756,0.002036868,0.002919984,0.001660151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001334503,"about_ca_system_score_gemma":0.002065668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01032531,"about_ca_topic_score_gemma":0.0101235,"domain_scores_codex":[0.9882519,0.005891887,0.001024551,0.001903509,0.002429626,0.0004985849],"domain_scores_gemma":[0.9252,0.05893051,0.002721081,0.006367553,0.005587712,0.00119304],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009374351,0.0003899033,0.1186043,0.001485077,0.001357974,0.0004310616,0.0005157833,0.2035703,0.002198848,0.01615314,0.03471369,0.6196426],"study_design_scores_gemma":[0.0000993528,0.0003925195,0.02869749,0.0004216058,0.0002131821,0.0003878196,0.0004817795,0.9033341,0.003703116,0.05075232,0.01143433,0.0000825034],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4657352,0.03549452,0.442497,0.02353816,0.001255855,0.0003332375,0.006194645,0.005281765,0.01966968],"genre_scores_gemma":[0.9073198,0.003709697,0.07773367,0.002229781,0.0003506992,0.00009864607,0.004884911,0.0002828664,0.003389882],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02835275,"threshold_uncertainty_score":0.1499455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03806340189915003,"score_gpt":0.3707510712470309,"score_spread":0.3326876693478809,"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."}}