{"id":"W4280621603","doi":"10.1101/2022.05.10.491396","title":"Fast and Accurate Bayesian Polygenic Risk Modeling with Variational Inference","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Medical Research Council; Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Markov chain Monte Carlo; Inference; Computer science; Bayesian probability; Genome-wide association study; Bayesian inference; Posterior probability; Statistical inference; Artificial intelligence; Machine learning; Statistics; Pattern recognition (psychology); Single-nucleotide polymorphism; Mathematics; Biology; Genetics","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":[0.003737186,0.0009064132,0.001486406,0.0007552775,0.0004497467,0.001152109,0.002637578,0.00123186,0.003223704],"category_scores_gemma":[0.01295706,0.001111914,0.001443597,0.0008688147,0.0007602046,0.001094389,0.001268606,0.002403396,0.0005562323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001002965,"about_ca_system_score_gemma":0.001977598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02549869,"about_ca_topic_score_gemma":0.02245862,"domain_scores_codex":[0.9989795,0.0006213298,0.00004288083,0.000141756,0.0001429574,0.00007163957],"domain_scores_gemma":[0.9931283,0.005895731,0.0002455017,0.0002534948,0.0003355754,0.0001413854],"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":[0.00006077687,0.00002445304,0.0019546,0.00005123718,0.00008737,0.00006453759,0.00005477078,0.9544646,0.0004901462,0.02265327,0.001407716,0.01868657],"study_design_scores_gemma":[0.000006433499,0.000002937394,0.00008404988,0.000003924937,0.000003037955,0.00000497693,0.000002558784,0.9936667,0.00004878111,0.006014502,0.0001589558,0.000003078468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01042686,0.0002593134,0.9877407,0.0002662734,0.00002621579,0.0000364738,0.0002168819,0.0003724243,0.0006548826],"genre_scores_gemma":[0.4502631,0.0004892708,0.5415692,0.0004486587,0.0001555676,0.0003081662,0.001851447,0.0004596555,0.004454981],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02549869,"threshold_uncertainty_score":0.05070055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01208267585326766,"score_gpt":0.2341061512745192,"score_spread":0.2220234754212516,"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."}}