{"id":"W4225293827","doi":"10.1371/journal.pcbi.1010007","title":"Inferring ongoing cancer evolution from single tumour biopsies using synthetic supervised learning","year":2022,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research","funders":"Ontario Ministry of Research and Innovation; Canadian Institutes of Health Research","keywords":"Inference; Computer science; Leverage (statistics); Artificial intelligence; Machine learning; Transfer of learning; Deep learning; Bayesian inference; Selection (genetic algorithm); Genomics; Cancer; ENCODE; Computational biology; Bioinformatics; Bayesian probability; Biology; Genome; Genetics; Gene","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":[0.001394171,0.0003710982,0.000330187,0.0003900585,0.0002650574,0.0006315518,0.001005999,0.0009078576,0.0008864551],"category_scores_gemma":[0.006851252,0.0003715707,0.0006409541,0.0002789661,0.0007064726,0.0005840534,0.0005676124,0.0009415442,0.0001899583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007223454,"about_ca_system_score_gemma":0.0006963511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003043232,"about_ca_topic_score_gemma":0.006696494,"domain_scores_codex":[0.9996639,0.0001209358,0.00001428167,0.0001137893,0.00006271571,0.00002430179],"domain_scores_gemma":[0.9965514,0.002489223,0.0003090178,0.0003454827,0.0001974781,0.0001074066],"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":[0.0001727422,0.00005988807,0.01859596,0.00009087377,0.0001001319,0.000191929,0.0001235932,0.9313551,0.01164884,0.009148111,0.001046663,0.02746621],"study_design_scores_gemma":[0.000007943833,0.00001579106,0.0009346993,0.000005400233,0.00000757526,0.00005634545,0.00001212726,0.9884382,0.003353575,0.006752394,0.0004089295,0.000007013424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3905315,0.0003171686,0.6051295,0.0004986927,0.00005165758,0.00005774868,0.0008519279,0.0009923346,0.001569431],"genre_scores_gemma":[0.8834438,0.0001174533,0.1136309,0.0002318158,0.00002688604,0.00008159196,0.00129066,0.0001274094,0.001049506],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003043232,"threshold_uncertainty_score":0.007373154,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02478885281379568,"score_gpt":0.2535496494526057,"score_spread":0.22876079663881,"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."}}