{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01192217,0.002640483,0.002603516,0.004454195,0.001432155,0.00361502,0.006115992,0.001681173,0.1508499],"category_scores_gemma":[0.03280279,0.002569119,0.003215,0.006709442,0.0009263905,0.002242825,0.006315307,0.003527166,0.1036486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001118388,"about_ca_system_score_gemma":0.004151415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004578328,"about_ca_topic_score_gemma":0.007803057,"domain_scores_codex":[0.9955776,0.001513445,0.0004880382,0.001167619,0.0008856356,0.0003677729],"domain_scores_gemma":[0.9864801,0.005827758,0.001647041,0.003395868,0.00198263,0.0006666564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003755914,0.00004100063,0.003123068,0.001119702,0.0004680269,0.0001788709,0.0002155417,0.001934872,0.002530428,0.00317701,0.9387485,0.04808734],"study_design_scores_gemma":[0.0008714505,0.0001270688,0.01171758,0.0005023393,0.0003950274,0.0008233377,0.00008400102,0.02714524,0.0152848,0.02961922,0.9129921,0.0004377145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.001883226,0.0004010237,0.2781525,0.001103524,0.000369641,0.0006505955,0.4389387,0.2718486,0.006652222],"genre_scores_gemma":[0.01332614,0.0004118654,0.3810952,0.001437067,0.0002639059,0.003557467,0.4950665,0.09799344,0.006848466],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1508499,"threshold_uncertainty_score":0.5046433,"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."}}