{"id":"W2171977579","doi":"10.1093/bioinformatics/btt485","title":"iBMQ: a R/Bioconductor package for integrated Bayesian modeling of eQTL data","year":2013,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Clinical Research Institute","funders":"National Human Genome Research Institute; National Institutes of Health","keywords":"Bioconductor; Expression quantitative trait loci; R package; Computer science; Bayesian probability; Markov chain Monte Carlo; Univariate; Data mining; Computational biology; Biology; Machine learning; Artificial intelligence; Single-nucleotide polymorphism; Gene; Genetics; Computational science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01306017,0.004876665,0.005155007,0.005400447,0.001527783,0.004676033,0.007681073,0.002748222,0.1152933],"category_scores_gemma":[0.04020185,0.002933448,0.004630274,0.006921233,0.001625764,0.002760895,0.005167689,0.005388952,0.07277671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001484451,"about_ca_system_score_gemma":0.006022063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008971779,"about_ca_topic_score_gemma":0.01069013,"domain_scores_codex":[0.9939361,0.002145067,0.000471317,0.001670243,0.001454616,0.0003226205],"domain_scores_gemma":[0.9838187,0.01031798,0.00134882,0.00220454,0.001771599,0.0005382592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007008143,0.0001018553,0.006333827,0.004878169,0.002379147,0.0004881278,0.0004536731,0.02013929,0.003329306,0.02959089,0.849378,0.08222684],"study_design_scores_gemma":[0.001135363,0.0001946023,0.009201236,0.001200108,0.001450715,0.0009737117,0.0001426263,0.1637009,0.004825979,0.2075852,0.6091529,0.0004366658],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.002105015,0.002221184,0.5942948,0.001524455,0.0006503772,0.0004755198,0.2034837,0.1885285,0.006716357],"genre_scores_gemma":[0.02781849,0.001912855,0.6617547,0.002071771,0.0004919269,0.005964183,0.1619834,0.1293451,0.008657526],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.1152933,"threshold_uncertainty_score":0.3856944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04416219096831347,"score_gpt":0.28701331923114,"score_spread":0.2428511282628266,"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."}}