{"id":"W2946766521","doi":"10.1093/nar/gkz474","title":"High efficiency error suppression for accurate detection of low-frequency variants","year":2019,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Ontario Institute for Cancer Research; University of Toronto; University Health Network","funders":"Lotte and John Hecht Memorial Foundation; Cancer Research Society","keywords":"Biology; DNA sequencing; Computational biology; Error detection and correction; Generalizability theory; Genetics; Bioinformatics; DNA; Computer science; Algorithm; Mathematics; Statistics","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":[0.006766379,0.001382593,0.001453918,0.001815186,0.000852392,0.001330881,0.001597794,0.0015806,0.002447693],"category_scores_gemma":[0.01491178,0.0008316605,0.001148527,0.001475354,0.001082294,0.001251135,0.002259502,0.00229247,0.002313473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006936699,"about_ca_system_score_gemma":0.001402389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009279348,"about_ca_topic_score_gemma":0.002762116,"domain_scores_codex":[0.9926523,0.00166123,0.0005136747,0.002121661,0.002673134,0.0003779481],"domain_scores_gemma":[0.9891074,0.004387471,0.001669496,0.002245034,0.002254394,0.0003362325],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003892515,0.00013008,0.006607441,0.0005931752,0.0001581953,0.0003241796,0.0006170814,0.005103702,0.8786274,0.00290051,0.001503555,0.1030456],"study_design_scores_gemma":[0.00002120438,0.0002429339,0.003609295,0.0000610464,0.0000821821,0.0007059673,0.00005472253,0.03544725,0.9506029,0.001351052,0.007733057,0.00008829743],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1319513,0.002923417,0.853917,0.000395099,0.0004415917,0.0002811669,0.0007127659,0.006267058,0.003110585],"genre_scores_gemma":[0.4158785,0.0008938236,0.576605,0.0005521501,0.0001185221,0.0003218361,0.001073775,0.001147597,0.003408793],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006766379,"threshold_uncertainty_score":0.03578448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02527388695510112,"score_gpt":0.328029640083793,"score_spread":0.3027557531286919,"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."}}