{"id":"W2952683761","doi":"10.1186/s12859-018-2289-9","title":"PRS-on-Spark (PRSoS): a novel, efficient and flexible approach for generating polygenic risk scores","year":2018,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Jewish General Hospital; Canadian Institute for Advanced Research; University of British Columbia; McGill University; Douglas Mental Health University Institute","funders":"Health Canada; Ludmer Centre for Neuroinformatics and Mental Health; Fondation Brain Canada; McGill University; Canadian Institute for Advanced Research","keywords":"Single-nucleotide polymorphism; SNP; Major depressive disorder; Software; Biology; Computer science; Computational biology; Statistics; Genetics; Mathematics; Gene; Genotype","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":[0.0005496215,0.0001761884,0.0001917318,0.00005573623,0.0003116524,0.00003917819,0.0001388766,0.0001963051,0.000003103469],"category_scores_gemma":[0.0004735893,0.0001484412,0.00008553178,0.00008311857,0.0001338625,0.000003207046,0.0001060409,0.00007180241,0.000009604653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001503082,"about_ca_system_score_gemma":0.00008155701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001241545,"about_ca_topic_score_gemma":0.00001818396,"domain_scores_codex":[0.9988958,0.00003596844,0.0003911236,0.0002311176,0.00009411378,0.0003519206],"domain_scores_gemma":[0.9991717,0.00006220336,0.0002552392,0.0003081594,0.0001136052,0.00008903298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001893437,0.002237902,0.3856364,0.001832499,0.001802883,5.188594e-7,0.009249225,0.2847826,0.1016498,0.007684663,0.114937,0.08829305],"study_design_scores_gemma":[0.001099654,0.000832742,0.009543168,0.00001125436,0.00004662939,0.00001121917,0.0004541921,0.9800607,0.004208371,0.00004014269,0.003401499,0.0002904109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4106289,0.0001569868,0.5877807,0.00001985622,0.0001018636,0.0003343793,0.00008092858,0.00001528305,0.0008810459],"genre_scores_gemma":[0.3740638,0.00005641321,0.6245303,0.0004077427,0.0004412152,0.00006917753,0.0001656558,0.00001906883,0.0002466921],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6952782,"threshold_uncertainty_score":0.6053255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02616168096070415,"score_gpt":0.2715539279775312,"score_spread":0.245392247016827,"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."}}