{"id":"W2493472693","doi":"10.1093/bioinformatics/btw487","title":"<i>genipe</i>: an automated genome-wide imputation pipeline with automatic reporting and statistical tools","year":2016,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Montreal Heart Institute","funders":"","keywords":"Computer science; Imputation (statistics); Data mining; Python (programming language); Software; Suite; Documentation; Missing data; Machine learning; Programming language","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006321857,0.0001583312,0.0002254885,0.00003052182,0.0001048911,0.00004190368,0.00008630439,0.0001406028,0.00001648362],"category_scores_gemma":[0.001534554,0.0001014419,0.00002684582,0.00006567418,0.00009589798,0.0000222746,0.00006010263,0.00004347174,0.0000154985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002126609,"about_ca_system_score_gemma":0.00008412165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007327863,"about_ca_topic_score_gemma":0.00002186602,"domain_scores_codex":[0.9984409,0.00006204737,0.0008936904,0.0001927202,0.0001206189,0.0002900177],"domain_scores_gemma":[0.9986262,0.0001004771,0.0007389055,0.0002715043,0.0001287575,0.0001342023],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001962123,0.00028105,0.3813263,0.0004070975,0.0004273364,0.00003186004,0.001442914,0.001225567,0.06811949,0.0005279362,0.01392707,0.5320872],"study_design_scores_gemma":[0.002168355,0.001461151,0.7652189,0.00006778978,0.0001239662,0.0002807943,0.0006551779,0.2196322,0.00248467,0.0003970395,0.006713622,0.0007964136],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7596302,0.00003528475,0.2394428,0.000209502,0.0000363898,0.0001862293,0.00005849209,0.00009199362,0.0003091061],"genre_scores_gemma":[0.8654892,0.00005266342,0.133532,0.0003578613,0.0000606863,0.00001729359,0.0003645472,0.00001772311,0.0001079997],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5312908,"threshold_uncertainty_score":0.4136682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01702282634354884,"score_gpt":0.2838568820767196,"score_spread":0.2668340557331707,"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."}}