{"id":"W3175892313","doi":"10.1038/s41586-021-03648-3","title":"Clonal fitness inferred from time-series modelling of single-cell cancer genomes","year":2021,"lang":"en","type":"article","venue":"Nature","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":171,"is_retracted":false,"has_abstract":false,"ca_institutions":"BC Cancer Agency; Canada's Michael Smith Genome Sciences Centre; Mount Sinai Hospital; Lunenfeld-Tanenbaum Research Institute; University of Toronto; University of British Columbia","funders":"National Human Genome Research Institute; National Cancer Institute; Medical Research Council; Cancer Research UK","keywords":"Biology; Fitness landscape; Somatic evolution in cancer; Epistasis; Genetics; Genetic Fitness; Genome; Population; Cancer; Evolutionary biology; Computational biology; Gene; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002525313,0.0001056606,0.0001398291,0.00001436624,0.00003043612,0.00001799721,0.0001089904,0.0005622788,0.0001307612],"category_scores_gemma":[0.00002817015,0.0001097523,0.00007501345,0.00006138231,0.00003553377,0.000002711681,0.00009629209,0.0002492421,0.000003165571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001610304,"about_ca_system_score_gemma":0.0002009531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004674325,"about_ca_topic_score_gemma":0.0001051462,"domain_scores_codex":[0.9993966,0.00001322386,0.0001308711,0.0002369248,0.00009459248,0.0001278055],"domain_scores_gemma":[0.9994763,0.00002075478,0.00006379424,0.0002235471,0.0001730758,0.00004254608],"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":[0.0000800434,0.00007857408,0.001639968,0.00002314438,0.00005977564,0.000007827917,0.00006075555,0.01257425,0.9802617,0.00006486692,0.004084528,0.001064521],"study_design_scores_gemma":[0.0003624071,0.00004908611,0.0002713872,0.00001826846,0.00003110326,0.000002166203,0.00003008774,0.000516879,0.8993685,0.0003465112,0.09884268,0.0001609703],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9403589,0.05578717,0.0009911766,0.0002845849,0.0003729034,0.00005573066,0.0006966393,0.000005885245,0.001447059],"genre_scores_gemma":[0.9908548,0.002777022,0.003217213,0.0004924791,0.0004903348,0.00000674715,0.0007247549,0.00002181883,0.001414838],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09475815,"threshold_uncertainty_score":0.4475567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01109094189505044,"score_gpt":0.2298262081097938,"score_spread":0.2187352662147433,"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."}}