{"id":"W2101342994","doi":"10.1002/gepi.20474","title":"The challenge of detecting epistasis (G×G Interactions): Genetic Analysis Workshop 16","year":2009,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Heart Institute","funders":"National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Center for Research Resources; National Institute of General Medical Sciences; National Institute on Drug Abuse; University of Washington; National Institute on Alcohol Abuse and Alcoholism; National Institute on Aging; National Institutes of Health; Fogarty International Center; National Heart, Lung, and Blood Institute","keywords":"Epistasis; Penetrance; Variety (cybernetics); Genome-wide association study; Computational biology; Variance (accounting); Genetic association; Biology; Evolutionary biology; Statistics; Computer science; Machine learning; Econometrics; Genetics; Artificial intelligence; Mathematics; Genotype; Gene; Phenotype; 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":[0.06933578,0.0009361962,0.002110163,0.001136706,0.001521204,0.005660974,0.001583821,0.004636322,0.004291862],"category_scores_gemma":[0.07342048,0.0005575462,0.0018811,0.0007125227,0.002743534,0.002729325,0.003654566,0.007633673,0.001761982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001651654,"about_ca_system_score_gemma":0.003580051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001276944,"about_ca_topic_score_gemma":0.001443612,"domain_scores_codex":[0.9809975,0.01428581,0.0005057175,0.001947633,0.001699888,0.0005634089],"domain_scores_gemma":[0.9279874,0.0556629,0.001809354,0.002954462,0.007105876,0.004479951],"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":[0.0008109502,0.0004359096,0.011566,0.0006937349,0.0007682387,0.001271773,0.001957362,0.01372411,0.008236784,0.06763441,0.3162389,0.5766618],"study_design_scores_gemma":[0.0004246834,0.0008270642,0.0183866,0.0007901209,0.0002982704,0.001788286,0.003636709,0.03569656,0.006609831,0.6279082,0.3032257,0.0004079318],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.04551279,0.01191111,0.4502315,0.4708967,0.01220867,0.0003946786,0.0007581792,0.0009505131,0.007135879],"genre_scores_gemma":[0.2905101,0.008207698,0.5719268,0.08147375,0.01520998,0.001258237,0.001320187,0.000986528,0.02910665],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.06933578,"threshold_uncertainty_score":0.3666871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03153113892671327,"score_gpt":0.3264643137842873,"score_spread":0.294933174857574,"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."}}