{"id":"W4382343881","doi":"10.1177/09622802231181231","title":"A Bayesian genomic selection approach incorporating prior feature ordering and population structures with application to coronary artery disease","year":2023,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Feature selection; Coronary artery disease; Disease; Bayesian probability; Population; Selection (genetic algorithm); Computer science; Medicine; Machine learning; Artificial intelligence; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006496974,0.0009260733,0.001649874,0.001665141,0.0008883186,0.00107414,0.002271145,0.001434332,0.002504993],"category_scores_gemma":[0.01435304,0.0007419011,0.001443273,0.002028355,0.0009994826,0.00138873,0.001504899,0.001632034,0.0005225447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001057912,"about_ca_system_score_gemma":0.00238949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01432676,"about_ca_topic_score_gemma":0.01553968,"domain_scores_codex":[0.9970035,0.00193512,0.00007659512,0.0004208598,0.0003901256,0.00017388],"domain_scores_gemma":[0.9938024,0.004831413,0.0002762442,0.0003094178,0.0005790089,0.000201508],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002739675,0.0001783237,0.008728223,0.0001137468,0.0003704765,0.0004399958,0.0002904023,0.6846465,0.00212966,0.07236075,0.003414444,0.2270534],"study_design_scores_gemma":[0.00005091807,0.00004678635,0.00102697,0.00001695351,0.00005257738,0.00007146229,0.0000165769,0.9698706,0.0002010918,0.02759074,0.001032045,0.00002313313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008659523,0.0002418332,0.9901285,0.0002441736,0.00002194195,0.00004366928,0.00006387885,0.000164269,0.000432294],"genre_scores_gemma":[0.4105038,0.001038149,0.580501,0.0006421822,0.0003606651,0.0005583802,0.001073621,0.0002045274,0.005117732],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01432676,"threshold_uncertainty_score":0.03435975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03723774442322512,"score_gpt":0.4451527032697196,"score_spread":0.4079149588464945,"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."}}