{"id":"W3081599354","doi":"10.1101/2020.08.31.276212","title":"PyClone-VI: Scalable inference of clonal population structures using whole genome data","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Inference; Computer science; Scalability; Software; Bayesian probability; Population; Bayesian inference; Statistical inference; Data mining; Computational biology; Artificial intelligence; Statistics; Biology; Mathematics; Database; Programming language; Medicine","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":[0.004998783,0.001132765,0.00156889,0.001860157,0.000878717,0.002490078,0.003225022,0.00154407,0.01204307],"category_scores_gemma":[0.01956244,0.001390012,0.002116705,0.001670464,0.0008638305,0.001552624,0.00225849,0.003008977,0.004475706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008680141,"about_ca_system_score_gemma":0.002757868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007262015,"about_ca_topic_score_gemma":0.009826556,"domain_scores_codex":[0.9986567,0.0005033461,0.00006164255,0.0003515321,0.0003517055,0.00007506611],"domain_scores_gemma":[0.9958565,0.002585741,0.0002609672,0.0007248364,0.0003743681,0.0001976444],"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":[0.001237755,0.0002283215,0.03177663,0.001454965,0.002250224,0.0008657724,0.0006971969,0.4017228,0.02594114,0.03511681,0.1268538,0.3718547],"study_design_scores_gemma":[0.0002781225,0.00005635764,0.003047489,0.00007951886,0.00008725662,0.0003657755,0.0000485271,0.9323583,0.005841739,0.04141976,0.0163442,0.00007286268],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01376935,0.0006458199,0.9423032,0.00036555,0.0001223588,0.0001521282,0.008998527,0.0321753,0.001467841],"genre_scores_gemma":[0.1225996,0.0004280516,0.842878,0.0005344917,0.0001947141,0.0005999886,0.02001994,0.01002301,0.002722242],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01204307,"threshold_uncertainty_score":0.04028815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03241505582465433,"score_gpt":0.2668076278688629,"score_spread":0.2343925720442085,"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."}}