{"id":"W6976966386","doi":"10.6084/m9.figshare.5126911.v1","title":"Supplementary Material for: 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":"dataset","venue":"Figshare","topic":"Knowledge Management in Higher Education","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Single-nucleotide polymorphism; Genetic association; Genome-wide association study; SNP; Bayesian network; Breast cancer; Candidate gene; Posterior probability; Graphical model","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005334927,0.0004274301,0.0006469968,0.0001668872,0.0009217638,0.000388466,0.000571909,0.0003433993,0.03634622],"category_scores_gemma":[0.0002824516,0.0004818645,0.0001267531,0.0003053356,0.00001162801,0.0001969985,0.0002925876,0.0002827833,0.00004713795],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004179961,"about_ca_system_score_gemma":0.0005364454,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5593932,"about_ca_topic_score_gemma":0.948074,"domain_scores_codex":[0.9968239,0.0004143307,0.0006138816,0.0008712503,0.0006256535,0.0006509476],"domain_scores_gemma":[0.9975939,0.0005688156,0.0006796976,0.0005481078,0.0004186535,0.0001908678],"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":[0.00007095431,0.0001121175,0.004566732,0.00005702403,0.0007092502,0.000008818893,0.003767807,0.003631603,0.000001279297,0.000001132054,0.9868725,0.0002007683],"study_design_scores_gemma":[0.001111516,0.00005496083,0.005345278,0.0007682368,0.001657693,8.790898e-7,0.0009858815,0.002962671,0.000001588695,0.0004084868,0.9858421,0.000860692],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.003064919,0.00005127393,0.0001415309,0.0003368049,0.001234058,0.004015441,0.9910938,0.0000573578,0.000004783289],"genre_scores_gemma":[0.02173216,0.000007301567,0.0003778403,0.0004198083,0.005739224,0.005529456,0.9661049,0.00004669565,0.0000426647],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3886809,"threshold_uncertainty_score":0.9997633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04966594364649182,"score_gpt":0.3617523148014005,"score_spread":0.3120863711549087,"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."}}