{"id":"W4402992718","doi":"10.1038/s41588-024-01934-0","title":"Valid inference for machine learning-assisted genome-wide association studies","year":2024,"lang":"en","type":"article","venue":"Nature Genetics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Jewish General Hospital; University of Toronto","funders":"Division of Graduate Education; National Human Genome Research Institute; National Institute on Aging; U.S. Department of Health and Human Services; National Institutes of Health; Wisconsin Alumni Research Foundation; University of Wisconsin-Madison","keywords":"Biology; Inference; Genome-wide association study; Computational biology; Association (psychology); Evolutionary biology; Genetic association; Genetics; Artificial intelligence; Single-nucleotide polymorphism; Computer science; Gene; Genotype; Epistemology","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":[0.05847357,0.001359332,0.004030967,0.003608662,0.002989119,0.004372386,0.006732727,0.003297938,0.005553926],"category_scores_gemma":[0.2858976,0.001906765,0.003568496,0.003642076,0.004400315,0.004997504,0.005914794,0.007784793,0.001505687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001577726,"about_ca_system_score_gemma":0.004776974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00397381,"about_ca_topic_score_gemma":0.00425001,"domain_scores_codex":[0.9495919,0.03699734,0.002901435,0.005570165,0.003971654,0.0009675043],"domain_scores_gemma":[0.6528522,0.3064432,0.004789149,0.02873382,0.005815548,0.001366061],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002981033,0.0004713833,0.04959248,0.00211241,0.004351116,0.002183381,0.0006746268,0.2424359,0.003620593,0.2869177,0.0176945,0.386965],"study_design_scores_gemma":[0.0002277698,0.0001013172,0.001613343,0.0001641236,0.0004079289,0.0003052842,0.00005246495,0.412316,0.001622346,0.579043,0.004108893,0.0000374513],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004954262,0.0008297967,0.990973,0.0008826339,0.0001761879,0.0001037943,0.0008204895,0.0007530851,0.0005068221],"genre_scores_gemma":[0.3532835,0.001096104,0.6318545,0.002364914,0.001146495,0.001141472,0.005950661,0.0005956992,0.002566674],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05847357,"threshold_uncertainty_score":0.3092415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02157001075427978,"score_gpt":0.3309692256446784,"score_spread":0.3093992148903986,"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."}}