{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002879767,0.0002450629,0.0006635942,0.0001536766,0.000117866,0.00001066851,0.0001795829,0.0006107654,0.00004882105],"category_scores_gemma":[0.007476312,0.000247111,0.0001141059,0.0003346557,0.00003925313,0.00001800456,0.0001354158,0.0002337871,0.00006021553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000343348,"about_ca_system_score_gemma":0.0002235652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003054413,"about_ca_topic_score_gemma":0.0002896841,"domain_scores_codex":[0.9963996,0.001126006,0.001153757,0.0004753468,0.0001115041,0.0007337971],"domain_scores_gemma":[0.9981074,0.0005736218,0.0006672546,0.0002997535,0.0002658105,0.00008613191],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003042811,0.00002996865,0.8868456,0.00003522701,0.0001347777,4.982648e-7,0.0001157782,0.09951577,0.009943418,0.0003064126,0.001563528,0.001478545],"study_design_scores_gemma":[0.001591212,0.0004534021,0.8276098,0.00003343408,0.00006877827,0.00004202556,0.0003593217,0.144525,0.0005389496,0.007795944,0.01639058,0.0005914977],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8741512,0.0008737425,0.1229237,0.0007464841,0.0006351056,0.0004510159,0.000006630521,0.0000230115,0.000189134],"genre_scores_gemma":[0.895026,0.0003183221,0.1022123,0.001512377,0.0003020123,0.00007122182,0.0001468639,0.00002078491,0.0003900968],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05923579,"threshold_uncertainty_score":0.9999981,"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."}}