{"id":"W4388199641","doi":"10.3389/fnagi.2023.1281748","title":"An interpretable Alzheimer’s disease oligogenic risk score informed by neuroimaging biomarkers improves risk prediction and stratification","year":2023,"lang":"en","type":"article","venue":"Frontiers in Aging Neuroscience","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; National Institute on Aging; Alzheimer's Association","keywords":"Neuroimaging; Alzheimer's Disease Neuroimaging Initiative; Disease; Cohort; Interpretability; Medicine; Framingham Risk Score; Oncology; Dementia; Internal medicine; Bioinformatics; Machine learning; Biology; Computer science; Psychiatry","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":[0.002547325,0.001348471,0.0007526489,0.001801005,0.0002483937,0.001026883,0.0003858465,0.0004662388,0.001473891],"category_scores_gemma":[0.005114688,0.0001679415,0.0007802611,0.0008486668,0.0002806468,0.0003913357,0.0006688301,0.0005626976,0.0003310138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003794247,"about_ca_system_score_gemma":0.0006806293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002575505,"about_ca_topic_score_gemma":0.003157643,"domain_scores_codex":[0.9991278,0.0004019536,0.00007364443,0.000191047,0.0001405884,0.00006506006],"domain_scores_gemma":[0.9985529,0.0006066731,0.0003769929,0.0001535506,0.0002104948,0.00009941646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002842974,0.0003606264,0.7783862,0.0001319277,0.001048371,0.0002891409,0.0001039165,0.05027981,0.008512704,0.0009809688,0.002401828,0.1546616],"study_design_scores_gemma":[0.0003865399,0.001294162,0.4814404,0.0001374031,0.001133558,0.0007484687,0.0001576154,0.492933,0.006795506,0.01219683,0.002648546,0.0001281433],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9620976,0.0005543531,0.03388535,0.0004081665,0.00004334785,0.00007719737,0.001308211,0.0002115653,0.001414092],"genre_scores_gemma":[0.9859446,0.0001393496,0.01235305,0.00006019214,0.00004133148,0.00003266124,0.001021382,0.00001765556,0.0003896989],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002575505,"threshold_uncertainty_score":0.01347172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01369830974375158,"score_gpt":0.2698440700258645,"score_spread":0.2561457602821129,"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."}}