{"id":"W3027831021","doi":"10.1093/biostatistics/kxab016","title":"Bayesian integrative analysis and prediction with application to atherosclerosis cardiovascular disease","year":2021,"lang":"en","type":"preprint","venue":"Biostatistics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Center for Advancing Translational Sciences; National Institutes of Health","keywords":"Disease; Atherosclerotic cardiovascular disease; Genome-wide association study; Computational biology; Genetic variants; Risk factor; Bioinformatics; Medicine; Gene; Biology; Genetics; Internal medicine; Single-nucleotide polymorphism; Genotype","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002112085,0.0002355663,0.0003967444,0.00009110395,0.00008527655,0.00005516189,0.0001080143,0.0002313323,0.000005318794],"category_scores_gemma":[0.0002389417,0.0002089575,0.00018528,0.0002225382,0.00006712162,0.000001382823,0.0002487912,0.0001292185,0.000001201869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004076567,"about_ca_system_score_gemma":0.0001325871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002855856,"about_ca_topic_score_gemma":0.0003727915,"domain_scores_codex":[0.9985276,0.0001609707,0.0002452641,0.0007340389,0.0001533028,0.0001788228],"domain_scores_gemma":[0.9987043,0.00002508351,0.0001298683,0.0006863551,0.0002608436,0.0001935855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001622791,0.0002041759,0.8391372,0.000199503,0.01853489,0.000008351255,0.0006903034,0.1089956,0.006127203,0.0002053271,0.002383388,0.02335174],"study_design_scores_gemma":[0.0002560219,0.0001885804,0.9767643,0.00004618295,0.004497517,0.000001685264,0.0003755051,0.0139859,0.0005014876,0.000153145,0.002791902,0.0004378408],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2007101,0.0006758973,0.7967131,0.0001370859,0.00006165404,0.0003434627,0.001306729,0.00001101341,0.00004102721],"genre_scores_gemma":[0.9405147,0.0009090627,0.05306805,0.0001446598,0.0001216273,0.0002029807,0.004942832,0.00002397604,0.00007213272],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.743645,"threshold_uncertainty_score":0.8521039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00791382655362262,"score_gpt":0.2394176628006027,"score_spread":0.2315038362469801,"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."}}