{"id":"W4304695497","doi":"10.1016/j.xgen.2022.100192","title":"Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease","year":2022,"lang":"en","type":"article","venue":"Cell Genomics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":407,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; Medical Research Council; Novo Nordisk Fonden; Wellcome Trust; Cooley's Anemia Foundation; National Human Genome Research Institute; Biogen; U.S. Department of Veterans Affairs","keywords":"Biobank; Genome-wide association study; Genetic association; Disease; Meta-analysis; Data science; Biology; Computational biology; Genetics; Computer science; Medicine; Gene; Genotype; Single-nucleotide polymorphism","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":[0.1758597,0.003471457,0.006121481,0.01623991,0.002034917,0.007264466,0.004659759,0.002183499,0.00577343],"category_scores_gemma":[0.2691689,0.002271445,0.01338812,0.01689297,0.001391641,0.003739864,0.01204408,0.003452063,0.0009375999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001351176,"about_ca_system_score_gemma":0.008186214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006656513,"about_ca_topic_score_gemma":0.008630308,"domain_scores_codex":[0.8595233,0.1162245,0.006795764,0.01089531,0.005489128,0.001071987],"domain_scores_gemma":[0.7420058,0.1770791,0.0122289,0.05595947,0.008385588,0.004341123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.005465151,0.0002516106,0.2347607,0.01174781,0.3862448,0.0008154094,0.002117801,0.01466876,0.005706885,0.01989011,0.06184191,0.256489],"study_design_scores_gemma":[0.008736058,0.001476708,0.1424458,0.00525616,0.3723471,0.002232861,0.001221342,0.06829634,0.01044571,0.2156906,0.17096,0.000891367],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06624881,0.04370619,0.7702898,0.01588379,0.002343404,0.003759602,0.07516728,0.01451269,0.008088441],"genre_scores_gemma":[0.3266654,0.005700532,0.6187979,0.003540039,0.001076516,0.008789029,0.03027759,0.003947414,0.001205622],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1758597,"threshold_uncertainty_score":0.9300464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03837515664813503,"score_gpt":0.308371599648952,"score_spread":0.269996443000817,"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."}}