{"id":"W4387430508","doi":"10.1016/j.ebiom.2023.104820","title":"Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data","year":2023,"lang":"en","type":"article","venue":"EBioMedicine","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Rosetrees Trust; National Institutes of Health; Genentech; U.S. National Library of Medicine; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Pfizer; BioClinica; Biogen; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; National Institute on Aging; Alzheimer's Association","keywords":"Random forest; Feature selection; Convolutional neural network; Artificial intelligence; Computer science; Feature (linguistics); Disease; Alzheimer's Disease Neuroimaging Initiative; Machine learning; Pattern recognition (psychology); Data mining; Bioinformatics; Alzheimer's disease; Medicine; Biology; Pathology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005415762,0.00013126,0.0002336899,0.0001775341,0.00008471149,0.000006535684,0.0002905229,0.00005727674,0.000009194148],"category_scores_gemma":[0.001403144,0.0001179924,0.00005530328,0.0004982825,0.00006906073,0.000003859159,0.0001656185,0.00004668953,0.000015348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006836727,"about_ca_system_score_gemma":0.00003898269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008182311,"about_ca_topic_score_gemma":0.000005552188,"domain_scores_codex":[0.9988249,0.0000368229,0.0002669679,0.0004733966,0.0001775527,0.0002203745],"domain_scores_gemma":[0.99882,0.0001049954,0.0001246414,0.0007037203,0.0001209943,0.0001256361],"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":[0.00009644863,0.00007334696,0.004339548,0.00009262584,0.0002145852,0.000001090176,0.00002623336,0.0003519075,0.9704597,0.0003405075,0.005739391,0.01826462],"study_design_scores_gemma":[0.001766649,0.0005371987,0.3078164,0.00003874591,0.0007267735,0.000001105886,0.000212259,0.02916917,0.0446227,0.000257046,0.6144451,0.0004068423],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.314868,0.02286739,0.6473189,0.01012171,0.0007817072,0.002925491,0.0007455064,0.0001485303,0.0002227502],"genre_scores_gemma":[0.990794,0.00055047,0.001313398,0.0001872843,0.000332555,0.0002528897,0.006448013,0.00002626229,0.00009513179],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.925837,"threshold_uncertainty_score":0.4811592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0358955819318269,"score_gpt":0.3184894024387611,"score_spread":0.2825938205069342,"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."}}