{"id":"W4362666447","doi":"10.1038/s43856-023-00269-x","title":"Deep learning-based polygenic risk analysis for Alzheimer’s disease prediction","year":2023,"lang":"en","type":"article","venue":"Communications Medicine","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"Parkwood Institute; St Joseph's Health Care; University of British Columbia; McGill University; Jewish General Hospital","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; H. Lundbeck A/S; Servier; National Natural Science Foundation of China; Eisai; Innovation and Technology Commission; Genentech; IXICO; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; University of Southern California; National Center for Advancing Translational Sciences; Meso Scale Diagnostics; Shenzhen Knowledge Innovation Program; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; F. Hoffmann-La Roche; University Grants Committee; Alzheimer's Drug Discovery Foundation; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Polygenic risk score; Artificial intelligence; Deep learning; Disease; Computer science; Machine learning; Computational biology; Medicine; Biology; Internal medicine; Genetics; Gene; Single-nucleotide polymorphism; Genotype","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":[0.003534376,0.0008638181,0.0009916764,0.00146364,0.0003929158,0.0007443292,0.0008837993,0.000784151,0.00118495],"category_scores_gemma":[0.008377695,0.0003517649,0.000986176,0.0009918946,0.0006102247,0.0007437075,0.0011114,0.001826204,0.0001918832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001278567,"about_ca_system_score_gemma":0.001596089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01059301,"about_ca_topic_score_gemma":0.008456747,"domain_scores_codex":[0.9990703,0.0005039261,0.00005619754,0.0001800641,0.00009987577,0.0000896104],"domain_scores_gemma":[0.995928,0.002985856,0.0004147278,0.0002231267,0.0003049786,0.0001433072],"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":[0.0002692237,0.0002219017,0.04197678,0.0000991357,0.0005615167,0.0002095519,0.00009806926,0.8466768,0.001208477,0.01173126,0.002436328,0.09451092],"study_design_scores_gemma":[0.000007756058,0.0000134172,0.001504484,0.00001056877,0.00002593175,0.00001495794,0.00000412839,0.9889117,0.0001482813,0.009202624,0.0001500081,0.000006191563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1823963,0.002092612,0.8112749,0.001685331,0.00007046192,0.00006445583,0.000858781,0.0004751554,0.001081885],"genre_scores_gemma":[0.9370927,0.0008512732,0.05939656,0.0003131979,0.0000705312,0.0001293954,0.000965793,0.00003874109,0.001141932],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01059301,"threshold_uncertainty_score":0.02106267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04493514788740757,"score_gpt":0.3355607507563277,"score_spread":0.2906256028689201,"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."}}