{"id":"W2275532802","doi":"10.1186/s12863-015-0313-x","title":"Filtering genetic variants and placing informative priors based on putative biological function","year":2016,"lang":"en","type":"article","venue":"BMC Genetics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Children's Hospital Research Institute of Manitoba","funders":"National Institute of Neurological Disorders and Stroke; National Institute of General Medical Sciences; University of Manitoba; U.S. Public Health Service; National Institutes of Health; Deutsche Forschungsgemeinschaft; National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute","keywords":"False discovery rate; Linkage disequilibrium; Prior probability; Computational biology; Biology; Computer science; Genetics; Artificial intelligence; Gene; Haplotype; Allele","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002413907,0.0001878836,0.0001734156,0.00006007319,0.0001137323,0.00001614964,0.0001013421,0.0002179859,0.00002352851],"category_scores_gemma":[0.0003557695,0.0001317303,0.00005784094,0.00006489506,0.0001075389,0.00000320392,0.00009873812,0.00005897293,0.00002329772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002426907,"about_ca_system_score_gemma":0.00006885794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003067028,"about_ca_topic_score_gemma":0.000008435727,"domain_scores_codex":[0.9987985,0.0001684719,0.000289043,0.0003478828,0.00009357747,0.0003024859],"domain_scores_gemma":[0.999301,0.000139526,0.0001475218,0.0002392229,0.00007497124,0.00009779634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004320806,0.0001020005,0.7465602,0.00003351638,0.0001140015,0.000002657223,0.0002227091,0.01299358,0.1972352,0.00008881355,0.0007427935,0.04147249],"study_design_scores_gemma":[0.001103119,0.001325606,0.9794907,0.0000241636,0.00002302903,0.000008514262,0.0001257803,0.006075377,0.009441388,0.0003491181,0.001742618,0.0002905846],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7837455,0.0001301981,0.215306,0.00008046124,0.0001214479,0.0001681648,0.00003061203,0.00001362476,0.0004039167],"genre_scores_gemma":[0.9785038,0.0001844921,0.02055383,0.000433064,0.000123349,0.00003101225,0.00003412178,0.00001614218,0.0001202248],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2329306,"threshold_uncertainty_score":0.5371807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02447670296635752,"score_gpt":0.2550769512244296,"score_spread":0.2306002482580721,"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."}}