{"id":"W2165194160","doi":"10.1186/1755-8794-5-12","title":"Evaluation of the imputation performance of the program IMPUTE in an admixed sample from Mexico City using several model designs","year":2012,"lang":"en","type":"article","venue":"BMC Medical Genomics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Banting and Best Diabetes Centre, University of Toronto; Instituto Mexicano del Seguro Social; Consejo Nacional de Ciencia y Tecnología; Canadian Institutes of Health Research; Ontario Innovation Trust","keywords":"Imputation (statistics); International HapMap Project; Missing data; Concordance; 1000 Genomes Project; Statistics; Genotype; Biology; Genetics; Genotyping; Mathematics; Single-nucleotide polymorphism; Gene","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":[0.02981502,0.000681947,0.0008882754,0.0007060866,0.0008333995,0.0009509338,0.00145908,0.001064557,0.001736602],"category_scores_gemma":[0.04141966,0.0004969304,0.001819067,0.001033721,0.0004361655,0.0004734795,0.001000035,0.001352778,0.000215507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001044382,"about_ca_system_score_gemma":0.001392851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007885021,"about_ca_topic_score_gemma":0.007004322,"domain_scores_codex":[0.9919836,0.006742641,0.0001504658,0.0007409111,0.0002461297,0.0001363674],"domain_scores_gemma":[0.9563571,0.03724082,0.001297656,0.002838946,0.001957324,0.0003080823],"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":[0.009217972,0.001179529,0.3319086,0.0007064352,0.007007556,0.000582045,0.001373401,0.4798636,0.005674567,0.009852676,0.003327717,0.149306],"study_design_scores_gemma":[0.001158546,0.003156448,0.1083205,0.00009976938,0.001598229,0.0003255049,0.0002421559,0.8727657,0.004750689,0.005013358,0.002462807,0.0001063775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7295367,0.0004456517,0.2663579,0.0003334193,0.00002907724,0.0003519561,0.001389412,0.0005732632,0.000982619],"genre_scores_gemma":[0.7965577,0.0002089427,0.1985788,0.0001435466,0.00001678928,0.0008073245,0.002938778,0.0001061294,0.0006418898],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02981502,"threshold_uncertainty_score":0.1576788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.116223278406938,"score_gpt":0.3578967278390289,"score_spread":0.2416734494320909,"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."}}