{"id":"W4396678436","doi":"10.1093/nar/gkae331","title":"Imputation Server PGS: an automated approach to calculate polygenic risk scores on imputation servers","year":2024,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Universität Innsbruck; Medizinische Universität Innsbruck","keywords":"Imputation (statistics); Population stratification; Biology; Trait; Computer science; Population; Web server; Statistics; Missing data; Genotype; Genetics; The Internet; Machine learning; Single-nucleotide polymorphism; World Wide Web; Demography; Gene; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00781958,0.002456406,0.001897644,0.002929835,0.001350344,0.004167505,0.004636128,0.001444304,0.0380444],"category_scores_gemma":[0.02975591,0.002268643,0.002309426,0.00524663,0.0006640662,0.002983186,0.004793202,0.00345439,0.03928863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009926017,"about_ca_system_score_gemma":0.003555963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004685008,"about_ca_topic_score_gemma":0.005093176,"domain_scores_codex":[0.9953394,0.001350973,0.0006029911,0.001015727,0.001370732,0.000320178],"domain_scores_gemma":[0.989222,0.003507949,0.0006777766,0.003512185,0.002504011,0.0005759258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001316993,0.0003006618,0.01580642,0.00053402,0.0007889873,0.0006001841,0.0004125876,0.02435231,0.004812257,0.01255345,0.6185576,0.3199645],"study_design_scores_gemma":[0.0008326685,0.0002242961,0.0106234,0.0002211125,0.0002287268,0.0007390269,0.0002236219,0.6220882,0.02407133,0.06885172,0.2714829,0.0004129527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004045722,0.0001834595,0.5848817,0.000428188,0.0002538446,0.0003934803,0.02879059,0.376855,0.004167987],"genre_scores_gemma":[0.07523416,0.0005631412,0.7140793,0.0007647024,0.0004026316,0.001679783,0.1490141,0.04810432,0.01015783],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0380444,"threshold_uncertainty_score":0.1272712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03553538870092986,"score_gpt":0.3729569205291889,"score_spread":0.3374215318282591,"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."}}