{"id":"W2015182193","doi":"10.1038/nmeth.2883","title":"PyClone: statistical inference of clonal population structure in cancer","year":2014,"lang":"en","type":"article","venue":"Nature Methods","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1081,"is_retracted":false,"has_abstract":false,"ca_institutions":"BC Cancer Agency; University of British Columbia","funders":"Canadian Institutes of Health Research","keywords":"Biology; Somatic cell; Inference; Computational biology; Approximate Bayesian computation; Cluster analysis; Population; Genetics; Bayesian probability; Somatic evolution in cancer; Statistical inference; Evolutionary biology; Cancer; Gene; Computer science; Artificial intelligence; Statistics; Mathematics","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":[0.01229532,0.0009484338,0.001987745,0.002016489,0.0011452,0.002198135,0.003535246,0.002556192,0.009881978],"category_scores_gemma":[0.05679935,0.001602994,0.00297607,0.002056888,0.001824134,0.002822283,0.002724678,0.003897789,0.002317458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008002891,"about_ca_system_score_gemma":0.002014288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003099473,"about_ca_topic_score_gemma":0.004064882,"domain_scores_codex":[0.9959465,0.002394859,0.0001833956,0.0007148793,0.0006107617,0.0001496128],"domain_scores_gemma":[0.9551157,0.03869102,0.000838413,0.004099561,0.0007632537,0.0004919611],"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":[0.001589244,0.0003708479,0.04159611,0.001019424,0.002527793,0.001034022,0.0008969342,0.4269272,0.01121826,0.08648951,0.04175845,0.384572],"study_design_scores_gemma":[0.000137942,0.00006096745,0.001366082,0.00002356266,0.00009137324,0.0001746624,0.00002527846,0.9479703,0.002481468,0.04491282,0.002732063,0.00002344306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01854402,0.0002490083,0.965076,0.0002089641,0.00008203665,0.00006129003,0.001510597,0.01374752,0.0005205357],"genre_scores_gemma":[0.3314749,0.0002745831,0.6523683,0.0005832602,0.0002994062,0.0007972622,0.005632499,0.006134355,0.002435435],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01229532,"threshold_uncertainty_score":0.06502467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009637945817347011,"score_gpt":0.3976675843009224,"score_spread":0.3880296384835754,"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."}}