{"id":"W3037798707","doi":"10.21203/rs.3.rs-629030/v1","title":"An Effector Index to Predict Target Genes at GWAS Loci","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Medical Research Council; Fonds de Recherche du Québec - Santé; Genentech; National Institutes of Health; National Institute for Health and Care Research; Jewish General Hospital; Novo Nordisk; Servier; European Commission; Canadian Institutes of Health Research; Compute Canada; Cancer Research UK; Wellcome Trust; GlaxoSmithKline; King's College London; Eli Lilly and Company","keywords":"Genome-wide association study; Effector; Index (typography); Computational biology; Gene; Biology; Genetics; Computer science; Single-nucleotide polymorphism; Genotype; World Wide Web","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002046204,0.0003146552,0.0004604974,0.0001846954,0.000287702,0.00009832531,0.0006958102,0.001026066,0.0003100628],"category_scores_gemma":[0.001226274,0.0003111146,0.0002377469,0.0002224354,0.0001249423,0.000002509592,0.002612307,0.0006883486,0.000101206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002161344,"about_ca_system_score_gemma":0.0006005983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003599848,"about_ca_topic_score_gemma":0.0005541929,"domain_scores_codex":[0.995302,0.001635319,0.0003763789,0.001205784,0.0005759003,0.0009046858],"domain_scores_gemma":[0.9970728,0.000112117,0.0001025763,0.001433377,0.0007953767,0.0004837828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002040183,0.0003265429,0.7304022,0.0005284512,0.000378171,0.00004517494,0.0002972341,0.02245993,0.175727,0.00001041883,0.06548793,0.004132952],"study_design_scores_gemma":[0.0008406691,0.002158314,0.7958369,0.0002226375,0.0000396034,0.00001888428,0.0006723873,0.003314425,0.04465978,0.0003011427,0.1508619,0.001073298],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898933,0.003789742,0.003391343,0.0006584878,0.000313176,0.0009755053,0.0003927087,0.00002816637,0.0005575281],"genre_scores_gemma":[0.9872531,0.0008649349,0.00260279,0.0001874677,0.001150862,0.000501157,0.005677078,0.00006710139,0.001695454],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1310672,"threshold_uncertainty_score":0.9999341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03310220783802668,"score_gpt":0.3834889373935158,"score_spread":0.3503867295554891,"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."}}