{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003757631,0.00007885457,0.0002191405,0.0000976719,0.00002672553,0.000007229169,0.00006326009,0.00009915257,0.000008819971],"category_scores_gemma":[0.0002924445,0.00006896591,0.00007377601,0.0001873501,0.00006421239,0.000002788993,0.00006501226,0.00002780327,0.000003263443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009299786,"about_ca_system_score_gemma":0.00005245737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003793334,"about_ca_topic_score_gemma":0.00005501589,"domain_scores_codex":[0.999346,0.00003281837,0.0003296655,0.00008362284,0.00007837742,0.0001294941],"domain_scores_gemma":[0.9994029,0.00001396347,0.0001723257,0.0002122933,0.0001067139,0.00009182416],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001263326,0.0002421682,0.8943771,0.0002183676,0.005193545,0.000001945497,0.004492567,0.007542043,0.01025949,0.002583733,0.03319975,0.04176297],"study_design_scores_gemma":[0.001905116,0.001100009,0.7569336,0.00002142683,0.002298716,0.00001618627,0.004353616,0.1739705,0.007533313,0.001151179,0.04984304,0.0008732305],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9508088,0.000480977,0.04564521,0.0002108357,0.00006101031,0.00008626744,0.00007188047,0.000007354672,0.0026277],"genre_scores_gemma":[0.9705375,0.0001100629,0.02886414,0.0001724249,0.00002518944,0.000003157781,0.000178676,0.000004727621,0.0001041464],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1664285,"threshold_uncertainty_score":0.2812348,"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."}}