{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001989086,0.0002966184,0.0007686529,0.0001698758,0.0003485859,0.000009305883,0.0005338048,0.0003524013,0.00006489697],"category_scores_gemma":[0.004793556,0.0002326113,0.0005531258,0.0004995385,0.000267958,0.000002767453,0.0001352783,0.0002618936,0.00001624213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005284476,"about_ca_system_score_gemma":0.00007397701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009331799,"about_ca_topic_score_gemma":0.0007974373,"domain_scores_codex":[0.9957709,0.001311143,0.00133545,0.0007130255,0.000109623,0.0007599003],"domain_scores_gemma":[0.9963017,0.001459035,0.0007895962,0.001091619,0.0002085231,0.0001495307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002296904,0.0002565425,0.3345163,0.0000215634,0.002996383,0.000005761239,0.0002133141,0.02964738,0.01025322,0.001291999,0.008484961,0.612083],"study_design_scores_gemma":[0.0005650492,0.0008787487,0.9387496,0.00001479632,0.0007995026,0.00004519648,0.0003490472,0.004244177,0.001122338,0.02306477,0.02962752,0.0005393109],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7976241,0.01743262,0.1723766,0.009140116,0.0005133928,0.0003911129,0.00002104529,0.00003344426,0.002467632],"genre_scores_gemma":[0.9715176,0.004085861,0.02246428,0.0008948556,0.0003612436,0.00004642453,0.00005032085,0.00001982955,0.0005596331],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6115436,"threshold_uncertainty_score":0.9485615,"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."}}