{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000544285,0.0001633714,0.0001573655,0.00001151073,0.0001494429,0.0000739321,0.0001682207,0.00008824664,0.0001039478],"category_scores_gemma":[0.00001443616,0.0001835518,0.0001056773,0.00005905864,0.00005579529,0.000002131719,0.0002250975,0.00008139884,0.00002336626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003429579,"about_ca_system_score_gemma":0.0001350513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005694703,"about_ca_topic_score_gemma":0.0004257748,"domain_scores_codex":[0.9989197,0.00002551255,0.0001981595,0.0004244784,0.00007869562,0.0003534075],"domain_scores_gemma":[0.9992906,0.000007705037,0.00008002476,0.0004141565,0.00008961038,0.0001178334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001001616,0.00005601093,0.009102131,0.0000369032,0.00002635467,0.00003915809,0.00008075246,0.00005928795,0.9879596,0.000004564759,0.0006776928,0.001947553],"study_design_scores_gemma":[0.0003517807,0.00005478617,0.01875112,0.000007463493,0.00001637367,0.00001597692,0.00005216117,0.00001145093,0.858819,0.00001673688,0.1216844,0.0002187512],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886321,0.009732475,0.0001070657,0.0000954284,0.0004160337,0.00009218649,0.0001659514,0.00001036934,0.0007484353],"genre_scores_gemma":[0.9942266,0.002194416,0.0003501929,0.001538091,0.0007817869,0.00003546672,0.0001843887,0.00003697438,0.0006520884],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1291406,"threshold_uncertainty_score":0.7485024,"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."}}