{"id":"W4287775941","doi":"10.48550/arxiv.2005.11586","title":"Bayesian Integrative Analysis and Prediction with Application to\\n Atherosclerosis Cardiovascular Disease","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; Emory University","keywords":"Disease; Atherosclerotic cardiovascular disease; Genome-wide association study; Genetic variants; Risk factor; Computational biology; Bioinformatics; Medicine; Gene; Biology; Genetics; Internal medicine; Single-nucleotide polymorphism; Genotype","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":[0.005275538,0.001017486,0.001598449,0.001354169,0.001124595,0.001757716,0.001675357,0.001155084,0.004015673],"category_scores_gemma":[0.01678988,0.0007187647,0.001842301,0.001583592,0.001190175,0.001299914,0.002400739,0.002532855,0.000919576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001750412,"about_ca_system_score_gemma":0.003178982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04142302,"about_ca_topic_score_gemma":0.03005022,"domain_scores_codex":[0.9982894,0.001012428,0.00006592218,0.0002756244,0.0002469334,0.0001096272],"domain_scores_gemma":[0.9934649,0.004992064,0.0003500426,0.0002917561,0.0006735758,0.0002275732],"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":[0.0001345351,0.0001328885,0.007593913,0.0001360654,0.000273163,0.0002041131,0.000275505,0.7855887,0.0007938712,0.08762234,0.005107067,0.1121379],"study_design_scores_gemma":[0.000007373355,0.0000101282,0.0003708944,0.00001117858,0.0000139174,0.00001181093,0.00001055712,0.973021,0.00005975139,0.02568275,0.0007907873,0.00000984982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0206958,0.001138602,0.972354,0.001423013,0.0001002917,0.00007138147,0.000287326,0.0008483637,0.003081137],"genre_scores_gemma":[0.4917465,0.002677895,0.4940777,0.0008520576,0.000604,0.0004522438,0.001691764,0.0005267812,0.007371047],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04142302,"threshold_uncertainty_score":0.08236384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02374709002643421,"score_gpt":0.1784424216581382,"score_spread":0.154695331631704,"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."}}