{"id":"W2360159874","doi":"10.1038/ng.3572","title":"A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune and neuropsychiatric diseases","year":2016,"lang":"en","type":"article","venue":"Nature Genetics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":70,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Centre for Addiction and Mental Health","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Human Genome Research Institute; National Institute of General Medical Sciences; National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Institute of Allergy and Infectious Diseases; Medical Research Council; National Institutes of Health; Juvenile Diabetes Research Foundation International; Asan Institute for Life Sciences, Asan Medical Center; National Health and Medical Research Council; Fulbright Canada; National Institute of Child Health and Human Development; Fondation Brain Canada","keywords":"Pleiotropy; Biology; Genetic heterogeneity; Schizophrenia (object-oriented programming); Rheumatoid arthritis; Genotype; Allele; Genetics; Major depressive disorder; Immunology; Medicine; Psychiatry; Phenotype; Gene; Endocrinology","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":[0.007496849,0.0009452867,0.001392387,0.003885957,0.0009996336,0.00211885,0.001313492,0.001363255,0.003338068],"category_scores_gemma":[0.02072446,0.00065808,0.002174147,0.002731527,0.001019811,0.001132428,0.001566085,0.002143612,0.0006039329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002876842,"about_ca_system_score_gemma":0.001005919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001828902,"about_ca_topic_score_gemma":0.001928045,"domain_scores_codex":[0.9970567,0.001400711,0.0001720042,0.0009165574,0.0003279596,0.0001261194],"domain_scores_gemma":[0.9812859,0.01368009,0.0009407043,0.002855574,0.0006637657,0.0005739504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002664295,0.0006384406,0.2920304,0.0006473465,0.006847823,0.001660672,0.001045136,0.02482911,0.0924062,0.03814214,0.008121137,0.5309672],"study_design_scores_gemma":[0.001053272,0.000935125,0.1638736,0.0001381601,0.002528939,0.003093329,0.0004474631,0.5468165,0.01890089,0.244326,0.01751747,0.0003692683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0869297,0.0004721678,0.9081848,0.0004720336,0.0001727018,0.0001344772,0.001495921,0.001177177,0.0009609752],"genre_scores_gemma":[0.4699739,0.0003050205,0.5245982,0.0003669783,0.0003334665,0.0004589268,0.001428772,0.0002957789,0.002238941],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007496849,"threshold_uncertainty_score":0.03964758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0103105137736539,"score_gpt":0.294531994132312,"score_spread":0.2842214803586581,"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."}}