{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001443641,0.000227751,0.0002905318,0.0002291241,0.0003682803,0.00002470974,0.00031601,0.0001072139,0.00005985888],"category_scores_gemma":[0.0001850827,0.0002338185,0.0001154927,0.0002577631,0.00006804294,0.00000290859,0.0003423353,0.0005240019,0.000002576528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001726165,"about_ca_system_score_gemma":0.00004623673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007946647,"about_ca_topic_score_gemma":0.000005736129,"domain_scores_codex":[0.9973803,0.001205134,0.0003483706,0.0005520073,0.0001834373,0.0003307534],"domain_scores_gemma":[0.9992845,0.00003735486,0.0001990859,0.0003619184,0.00004852216,0.00006862243],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002122182,0.00005640136,0.05050632,0.0000248986,0.0001784396,0.00000188802,0.0009584158,0.1609237,0.4797072,0.00002043437,0.00173465,0.3056755],"study_design_scores_gemma":[0.0007755115,0.0005502678,0.003350486,0.00001241625,0.00005409493,0.000004922386,0.004699559,0.05343851,0.1827753,0.00009570386,0.7537943,0.0004489569],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2945945,0.07044826,0.6287838,0.0005618589,0.003315736,0.0004835352,0.00001046707,0.00006264736,0.001739159],"genre_scores_gemma":[0.5081913,0.005484236,0.4782052,0.0006489228,0.0002461562,0.0001728536,0.00008031218,0.0000977541,0.006873277],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7520596,"threshold_uncertainty_score":0.9534841,"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."}}