{"id":"W3111170609","doi":"10.1186/s12859-020-03919-2","title":"PyClone-VI: scalable inference of clonal population structures using whole genome data","year":2020,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":140,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; BC Cancer Agency","funders":"Michael Smith Health Research BC","keywords":"Deconvolution; Computer science; Inference; Scalability; Software; Population; Computational biology; Bayesian probability; Genome; Data mining; Biology; Artificial intelligence; Algorithm; Genetics; Gene; Database","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":[0.004121173,0.001444365,0.001671173,0.001612788,0.001016367,0.002413024,0.003952279,0.001928734,0.0119395],"category_scores_gemma":[0.01731097,0.001573372,0.002587715,0.001435468,0.0009753974,0.001610971,0.002394189,0.00317283,0.004872367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001107965,"about_ca_system_score_gemma":0.003144164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009232974,"about_ca_topic_score_gemma":0.01476681,"domain_scores_codex":[0.9989986,0.0003164192,0.00005398925,0.000310113,0.0002524138,0.00006848496],"domain_scores_gemma":[0.9959612,0.002603887,0.0002580175,0.0005522097,0.0004033692,0.0002212953],"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":[0.001352686,0.0003039008,0.03236539,0.001913925,0.002594836,0.001032079,0.0009723249,0.4245451,0.0308012,0.02796244,0.1226629,0.3534932],"study_design_scores_gemma":[0.0001821726,0.00005508693,0.002330649,0.00005401579,0.00007401024,0.000292786,0.00005171203,0.960693,0.004621709,0.02162168,0.009971089,0.00005219322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0160891,0.0006850199,0.9208664,0.0004286784,0.000150174,0.0001698277,0.006841747,0.0533709,0.001398208],"genre_scores_gemma":[0.1112474,0.0004308636,0.8586118,0.0005358253,0.000155176,0.0006049571,0.01563802,0.0100758,0.002700121],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0119395,"threshold_uncertainty_score":0.03994161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.056188144556108,"score_gpt":0.2945795792089386,"score_spread":0.2383914346528306,"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."}}