{"id":"W3185928481","doi":"10.1155/2021/3359103","title":"Use of Deep-Learning Genomics to Discriminate Healthy Individuals from Those with Alzheimer’s Disease or Mild Cognitive Impairment","year":2021,"lang":"en","type":"article","venue":"Behavioural Neurology","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; University of California, San Diego; Genentech; National Institutes of Health; H. Lundbeck A/S; Servier; National Natural Science Foundation of China; Eisai; Takeda Pharmaceutical Company; IXICO; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; Roche; University of Southern California; F. Hoffmann-La Roche; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Higher Education Discipline Innovation Project; AbbVie; Alzheimer's Association; Foundation for the National Institutes of Health; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics","keywords":"Disease; Cognitive impairment; Cognition; Alzheimer's disease; Genomics; Psychology; Neuroscience; Developmental psychology; Cognitive psychology; Medicine; Biology; Genetics; Internal medicine; Gene; Genome","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001729843,0.0007206887,0.0004831891,0.001143438,0.0002423606,0.0004900512,0.0005010761,0.0006106409,0.0005967387],"category_scores_gemma":[0.003083565,0.0001557885,0.0004646894,0.0004900215,0.0003269011,0.0004767089,0.0005478481,0.0005110075,0.0001630948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006765433,"about_ca_system_score_gemma":0.0004758158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004160051,"about_ca_topic_score_gemma":0.005097513,"domain_scores_codex":[0.9994561,0.0002541826,0.00002667004,0.0001452711,0.00005117019,0.00006662957],"domain_scores_gemma":[0.9989246,0.0005676278,0.0001513193,0.0001105095,0.0001563553,0.00008955333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002589515,0.001480451,0.6233562,0.000149984,0.0007887753,0.0003800474,0.0002325768,0.08036442,0.01770856,0.001693832,0.002268375,0.2689873],"study_design_scores_gemma":[0.0001581871,0.0006590814,0.1842065,0.00003235742,0.0002031509,0.0002652915,0.0001168632,0.8011932,0.007153572,0.005171251,0.000801151,0.0000394966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9689553,0.0003206825,0.02852089,0.0005597172,0.00002220571,0.00004506451,0.0007246492,0.0001756915,0.0006758596],"genre_scores_gemma":[0.9905345,0.00004939245,0.008395984,0.0001148258,0.00001129226,0.00001984677,0.0006677508,0.000006123513,0.0002001392],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004160051,"threshold_uncertainty_score":0.009148419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07354794598960807,"score_gpt":0.3147759297401495,"score_spread":0.2412279837505414,"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."}}