{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02907775,0.001922723,0.003486402,0.004666427,0.001777479,0.002305894,0.004306003,0.002549991,0.008389826],"category_scores_gemma":[0.1213364,0.0009049273,0.007001269,0.0037008,0.002608733,0.001781603,0.003675272,0.004460678,0.0009483506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006971937,"about_ca_system_score_gemma":0.002641898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002370671,"about_ca_topic_score_gemma":0.001987767,"domain_scores_codex":[0.961471,0.02891533,0.001519658,0.004662677,0.002722508,0.0007088873],"domain_scores_gemma":[0.8268938,0.1590935,0.003969205,0.006409785,0.002129093,0.001504626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01019395,0.001681879,0.2971889,0.002633674,0.02720824,0.00521604,0.001175942,0.1658942,0.01132443,0.04626791,0.02247589,0.4087389],"study_design_scores_gemma":[0.001588687,0.002229916,0.04762203,0.0002240552,0.003138197,0.002102362,0.0002649487,0.8655739,0.004887988,0.0627441,0.009348504,0.0002753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1198528,0.001539512,0.8643125,0.001273544,0.0005066922,0.0009989929,0.0039458,0.004700202,0.002869949],"genre_scores_gemma":[0.5549825,0.0003402609,0.4337556,0.0009669097,0.0002771826,0.002909763,0.004773065,0.0005787031,0.001415983],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02907775,"threshold_uncertainty_score":0.1537797,"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."}}