{"id":"W3142571537","doi":"10.1016/j.cell.2021.03.009","title":"Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes","year":2021,"lang":"en","type":"article","venue":"Cell","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":557,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; Vector Institute; Ontario Institute for Cancer Research; University of Toronto","funders":"National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; H2020 European Research Council; Ono Pharmaceutical; Fonds Wetenschappelijk Onderzoek; Royal Society; Cancer Research UK; Li Ka Shing Foundation; Ovarian Cancer Research Fund Alliance; Francis Crick Institute; Wellcome Trust; Engineering and Physical Sciences Research Council; Bristol-Myers Squibb; AstraZeneca; Medical Research Council; Celgene; GlaxoSmithKline; Pfizer","keywords":"Biology; Genome; Genetics; Cancer; Computational biology; Gene; Mutation Accumulation; Mechanism (biology); Positive selection; Selection (genetic algorithm)","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":[0.0005310939,0.0002837749,0.0004521307,0.001466565,0.0004601449,0.0007136342,0.000247873,0.0004409954,0.00151861],"category_scores_gemma":[0.001831571,0.0002379845,0.0003748417,0.00218608,0.0002437503,0.0003022014,0.0006253479,0.0004624523,0.0004929768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003044278,"about_ca_system_score_gemma":0.0003852619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002374863,"about_ca_topic_score_gemma":0.0082533,"domain_scores_codex":[0.9995646,0.00005977067,0.000027236,0.0001810913,0.0001110475,0.0000561524],"domain_scores_gemma":[0.9991331,0.0003787414,0.0001913892,0.0001195584,0.0001019147,0.00007533418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0006626576,0.00008217384,0.3205048,0.001346479,0.00121704,0.001016895,0.0009451429,0.005244093,0.5582455,0.00223215,0.004439992,0.1040631],"study_design_scores_gemma":[0.0000349277,0.0001638911,0.8972319,0.0001388708,0.0005691362,0.00294981,0.0004333252,0.006375433,0.04144001,0.003256472,0.04735237,0.00005384394],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9561998,0.004634366,0.01312218,0.0002401241,0.00002094946,0.0000414489,0.02296053,0.0002909157,0.002489615],"genre_scores_gemma":[0.9364004,0.002644018,0.0142767,0.0002643752,0.00002099264,0.00005855262,0.04503476,0.0002080742,0.001092167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002374863,"threshold_uncertainty_score":0.005080223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01301866863085569,"score_gpt":0.2737606735147162,"score_spread":0.2607420048838605,"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."}}