{"id":"W2946448901","doi":"10.1002/gepi.22213","title":"Bayesian variable selection using partially observed categorical prior information in fine‐mapping association studies","year":2019,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Cancer Research UK; Breast Cancer Research Foundation","keywords":"Categorical variable; Prior probability; Bayesian probability; Single-nucleotide polymorphism; Posterior probability; Feature selection; Computer science; Bayes' theorem; SNP; Computational biology; Mathematics; Artificial intelligence; Pattern recognition (psychology); Biology; Machine learning; Genetics; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04376677,0.001097997,0.002629721,0.00289727,0.00107716,0.002662795,0.003912024,0.002253632,0.003396517],"category_scores_gemma":[0.1006262,0.001389585,0.002778179,0.004000724,0.003115598,0.002537429,0.003430038,0.003742289,0.0004537563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001223398,"about_ca_system_score_gemma":0.00200662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005015055,"about_ca_topic_score_gemma":0.00667626,"domain_scores_codex":[0.9678558,0.02750529,0.000786493,0.002005429,0.001445832,0.0004011711],"domain_scores_gemma":[0.8831224,0.1084037,0.00226275,0.004198947,0.001432326,0.0005799646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001068229,0.0002410922,0.03078459,0.0008144563,0.002063373,0.000750113,0.00151616,0.3689438,0.002554496,0.2185535,0.002922999,0.3697872],"study_design_scores_gemma":[0.0004858774,0.0001712445,0.007127364,0.0002402152,0.0003933907,0.0003197427,0.00011319,0.6406623,0.001325759,0.3441527,0.004872969,0.0001352678],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006799907,0.0003833137,0.9920017,0.0002248612,0.00002043908,0.00007693688,0.00007953415,0.0001749373,0.0002383542],"genre_scores_gemma":[0.2676412,0.001102709,0.7276105,0.0005214813,0.0001818923,0.0009043965,0.0007194857,0.0001768931,0.001141455],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04376677,"threshold_uncertainty_score":0.2314636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05280400568656481,"score_gpt":0.3045078020334335,"score_spread":0.2517037963468687,"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."}}