{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006505814,0.0002114337,0.0003980778,0.00003151574,0.0003829968,0.00002910783,0.0001386392,0.0002315681,0.00002880027],"category_scores_gemma":[0.0001227491,0.0002141855,0.00009045296,0.00008343681,0.00002386647,0.000006222462,0.000124355,0.0001720121,0.000002784502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003919012,"about_ca_system_score_gemma":0.00008443437,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.09907608,"about_ca_topic_score_gemma":0.5020621,"domain_scores_codex":[0.9982011,0.0003174435,0.0004014015,0.0005613284,0.0001517173,0.000366992],"domain_scores_gemma":[0.9989356,0.00009360628,0.000272828,0.0003862071,0.0001860083,0.0001257099],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003950759,0.00008380487,0.9208649,0.000001898835,0.0004720858,0.000003621424,0.0005143386,0.06989801,0.006477124,0.000006987131,0.001090365,0.0005473484],"study_design_scores_gemma":[0.001075193,0.0001137517,0.9561709,0.00001170302,0.0002972841,0.000005901091,0.0001332356,0.04001277,0.00006685579,0.001545925,0.000265384,0.0003011395],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9379022,0.0001100421,0.06080549,0.0001699667,0.0002093642,0.0004420459,0.000321357,0.00002095585,0.00001854059],"genre_scores_gemma":[0.995589,0.00000997239,0.001866383,0.0004760546,0.001671749,0.0001617462,0.0001493792,0.00002419027,0.00005158495],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.402986,"threshold_uncertainty_score":0.9069232,"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."}}