{"id":"W2232721574","doi":"10.1093/bioinformatics/btv504","title":"JBASE: Joint Bayesian Analysis of Subphenotypes and Epistasis","year":2015,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"Canadian Institutes of Health Research; Sickkids Research Institute; Hospital for Sick Children; Natural Sciences and Engineering Research Council of Canada; Instituto Mexicano del Seguro Social; Instituto de Seguriidad y Servicios Sociales de los Trabadores del Estado; Consejo Nacional de Ciencia y Tecnología","keywords":"Epistasis; Heritability; Genome-wide association study; Genotyping; Bayesian probability; Biology; Sample size determination; Genetic association; Computational biology; Genetics; Genotype; Missing heritability problem; Penetrance; Evolutionary biology; Phenotype; Genetic variants; Computer science; Statistics; Gene; Artificial intelligence; Mathematics; Single-nucleotide polymorphism","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.01119597,0.001560212,0.00228401,0.002120686,0.00114298,0.002666266,0.00403397,0.001609073,0.006965098],"category_scores_gemma":[0.03699561,0.001374838,0.003527037,0.002497955,0.001752694,0.002140129,0.004181968,0.004046561,0.001943293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00104432,"about_ca_system_score_gemma":0.003083925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01866439,"about_ca_topic_score_gemma":0.01890215,"domain_scores_codex":[0.9944402,0.00359582,0.0002046098,0.0008474945,0.0006630988,0.0002487574],"domain_scores_gemma":[0.9807708,0.01492765,0.0009877123,0.001734996,0.001100729,0.0004780781],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002052667,0.0004161154,0.05923957,0.001160459,0.004170892,0.0008475063,0.001045528,0.4172671,0.009368975,0.1070826,0.03063762,0.366711],"study_design_scores_gemma":[0.0002041945,0.00009359061,0.006220828,0.00007296682,0.0003437384,0.0002285481,0.00007708141,0.8851802,0.001195173,0.09980938,0.006497065,0.00007726732],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00921279,0.0003158947,0.9870036,0.0004867486,0.00003237663,0.0001169519,0.001030207,0.001231912,0.0005694125],"genre_scores_gemma":[0.2241035,0.0007487442,0.7615925,0.0009331509,0.000191873,0.0008899312,0.006989395,0.001103516,0.003447352],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01866439,"threshold_uncertainty_score":0.05921066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02499050603058147,"score_gpt":0.2642842934742861,"score_spread":0.2392937874437046,"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."}}