{"id":"W1597892494","doi":"10.1002/gepi.21735","title":"SBERIA: Set‐Based Gene‐Environment Interaction Test for Rare and Common Variants in Complex Diseases","year":2013,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research","funders":"National Human Genome Research Institute; National Heart, Lung, and Blood Institute; Canadian Institutes of Health Research; U.S. Public Health Service; National Institute on Aging; National Cancer Institute; National Institutes of Health; Groupement des Entreprises Françaises dans la lutte contre le Cancer","keywords":"Set (abstract data type); Correlation; Benchmark (surveying); Computer science; Identification (biology); Computational biology; Genome-wide association study; Type I and type II errors; Test set; Logistic regression; Single-nucleotide polymorphism; Statistical power; Data mining; Biology; Genetics; Artificial intelligence; Machine learning; Genotype; Statistics; Gene; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005067923,0.0002489859,0.0005422613,0.00007696047,0.0001079007,0.000009088236,0.0001698855,0.0003175933,0.0001403395],"category_scores_gemma":[0.001483404,0.0002396154,0.0001060081,0.0000508819,0.0001667743,0.000004570012,0.0001284236,0.0001060288,0.00003212907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004528283,"about_ca_system_score_gemma":0.00004191706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003174135,"about_ca_topic_score_gemma":0.000134455,"domain_scores_codex":[0.9975305,0.0005523001,0.000692156,0.0006350539,0.0000455617,0.0005444734],"domain_scores_gemma":[0.9981582,0.0009601314,0.000269733,0.000405635,0.00004548936,0.0001607722],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004336234,0.0001204713,0.9456456,0.00002656137,0.00004532397,0.000001305483,0.00001876259,0.004347514,0.03230214,0.00003296541,0.008675117,0.008740827],"study_design_scores_gemma":[0.0009866307,0.0004743549,0.9678679,0.000007167836,0.00003447714,0.0000181354,0.00005608714,0.01915773,0.0003119758,0.002946604,0.007890011,0.0002489432],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9695762,0.001445407,0.0246784,0.002956854,0.0001636157,0.0009063731,0.0001622483,0.00001473581,0.00009611543],"genre_scores_gemma":[0.9541447,0.0004891227,0.04096994,0.002602465,0.0002063237,0.0005500686,0.0009030215,0.00002998545,0.0001043354],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03199016,"threshold_uncertainty_score":0.9771231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03815932566874492,"score_gpt":0.3035945162868027,"score_spread":0.2654351906180578,"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."}}