{"id":"W3113128783","doi":"10.1038/s41467-020-20055-w","title":"Quantifying the influence of mutation detection on tumour subclonal reconstruction","year":2020,"lang":"en","type":"article","venue":"Nature Communications","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Princess Margaret Cancer Centre; Ontario Institute for Cancer Research; University of Toronto; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada; Prostate Cancer Canada; National Cancer Institute; National Institutes of Health; Government of Canada; Canadian Institutes of Health Research; Genome Canada; University of Toronto; Movember Foundation","keywords":"Tumour heterogeneity; Biology; Computational biology; Sampling (signal processing); Somatic cell; Cancer; Prostate cancer; Mutation; Genome; Evolutionary biology; Genetics; Gene; Computer science","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":[0.009938122,0.001066022,0.0007762279,0.001222381,0.0006166426,0.002068661,0.0008023111,0.001166217,0.0007189233],"category_scores_gemma":[0.02578595,0.0007710764,0.001098667,0.0008065811,0.000962323,0.001479212,0.001657994,0.001041598,0.0004112582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009506127,"about_ca_system_score_gemma":0.001237937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006108197,"about_ca_topic_score_gemma":0.008582791,"domain_scores_codex":[0.9967367,0.00111116,0.0002199058,0.0008939184,0.0007151637,0.0003231247],"domain_scores_gemma":[0.9865551,0.01017879,0.0008132185,0.001200494,0.000979753,0.0002725678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001787006,0.0001454729,0.3965176,0.0004805022,0.001217727,0.0003178956,0.0007346676,0.3947415,0.1009781,0.001732865,0.001603244,0.09974348],"study_design_scores_gemma":[0.00006392824,0.0003676655,0.07634269,0.00004769924,0.0002948607,0.0003672072,0.0002500585,0.8500189,0.06705887,0.00324668,0.001873202,0.00006821968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.914219,0.0008407701,0.08055667,0.0002451294,0.0000317751,0.00008285612,0.0007250283,0.002048198,0.001250625],"genre_scores_gemma":[0.9438367,0.000166488,0.05306636,0.0001603925,0.00000730664,0.00005052826,0.001900433,0.0004028226,0.0004090025],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009938122,"threshold_uncertainty_score":0.05255842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02398135543306031,"score_gpt":0.2925926180648503,"score_spread":0.26861126263179,"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."}}