{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001853684,0.0006263583,0.0007425318,0.002687681,0.0002204785,0.001250364,0.0005908881,0.0008194322,0.004877977],"category_scores_gemma":[0.005846002,0.0002257982,0.0005644528,0.001509554,0.0002488106,0.000621937,0.0008497268,0.000620041,0.001866052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000235493,"about_ca_system_score_gemma":0.0004048771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000582343,"about_ca_topic_score_gemma":0.0005044679,"domain_scores_codex":[0.9995728,0.0001107967,0.00003684243,0.0001164843,0.000117659,0.00004545486],"domain_scores_gemma":[0.997305,0.001466518,0.0002462335,0.000455923,0.000304576,0.0002217303],"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":[0.003382819,0.0007662636,0.4424565,0.0004487108,0.0007992973,0.0007885708,0.0001637981,0.04770409,0.05550715,0.02417803,0.01542716,0.4083777],"study_design_scores_gemma":[0.0004751236,0.0007300783,0.1907841,0.00007974055,0.0005957859,0.001222248,0.0001355923,0.6741676,0.02525833,0.0929854,0.01346545,0.0001005122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3957362,0.001220033,0.5720304,0.0006922918,0.0001956348,0.0002349374,0.0159815,0.004146073,0.00976293],"genre_scores_gemma":[0.844658,0.0004328095,0.1367588,0.0002077245,0.0002229981,0.0001848035,0.01176139,0.0003342426,0.005439224],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004877977,"threshold_uncertainty_score":0.0163185,"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."}}