{"id":"W2061274432","doi":"10.1159/000365923","title":"Inferring Gene Network from Candidate SNP Association Studies Using a Bayesian Graphical Model: Application to a Breast Cancer Case-Control Study from Ontario","year":2014,"lang":"en","type":"article","venue":"Human Heredity","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto; Lunenfeld-Tanenbaum Research Institute; The Scarborough Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; National Cancer Institute; Cancer Care Ontario","keywords":"Single-nucleotide polymorphism; Genetic association; SNP; Posterior probability; Genome-wide association study; Candidate gene; Computational biology; Breast cancer; Biology; Bayesian network; Markov chain Monte Carlo; Genetics; Bayesian probability; Bioinformatics; Computer science; Gene; Cancer; Artificial intelligence; Genotype","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":[0.003191503,0.0003982879,0.000495953,0.001107553,0.001509357,0.0006469616,0.0009895675,0.0005990333,0.002151102],"category_scores_gemma":[0.01140103,0.0004359263,0.0009248873,0.00156728,0.0007358218,0.000172767,0.0004947902,0.0005346338,0.0001204439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008920788,"about_ca_system_score_gemma":0.006464494,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8961216,"about_ca_topic_score_gemma":0.8908364,"domain_scores_codex":[0.9988966,0.0006459994,0.00004570652,0.0001679674,0.0001560274,0.00008764865],"domain_scores_gemma":[0.9959561,0.003089257,0.0002927043,0.0002424545,0.000318175,0.0001012644],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001119062,0.0003239034,0.6894712,0.0002883116,0.001058442,0.006032531,0.002181076,0.221927,0.004172603,0.01062704,0.003181872,0.05961685],"study_design_scores_gemma":[0.0006184083,0.0001629526,0.3320152,0.00007733455,0.0006445865,0.001199419,0.0009211586,0.6478631,0.0009710705,0.01106992,0.004378016,0.00007884939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9265493,0.0004185956,0.06839812,0.0007356607,0.00001150251,0.0003613289,0.001652392,0.0001306626,0.001742491],"genre_scores_gemma":[0.9700118,0.0003048052,0.02760408,0.00005808101,0.000007354311,0.0001281188,0.0009253254,0.00001447172,0.0009458436],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1038784,"threshold_uncertainty_score":0.2089803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02882455763125636,"score_gpt":0.3140252464581179,"score_spread":0.2852006888268615,"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."}}