{"id":"W4386996573","doi":"10.3390/genes14101856","title":"Decoding Cancer Evolution: Integrating Genetic and Non-Genetic Insights","year":2023,"lang":"en","type":"review","venue":"Genes","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Princess Margaret Cancer Centre; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Princess Margaret Cancer Foundation","keywords":"Multicellular organism; Biology; Cancer; Somatic evolution in cancer; Computational biology; Adaptability; Cancer cell; Evolutionary biology; Cell; Genetics; Ecology","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":[0.0009897546,0.0009251953,0.001641792,0.002361777,0.00030705,0.001545443,0.001079263,0.001990869,0.002996277],"category_scores_gemma":[0.001552516,0.000350306,0.0005735374,0.001691919,0.0009423366,0.002394112,0.0008754969,0.002722093,0.001916757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001158139,"about_ca_system_score_gemma":0.001342608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001253551,"about_ca_topic_score_gemma":0.002067199,"domain_scores_codex":[0.9996898,0.00007485866,0.00003151131,0.00005588669,0.0001118322,0.00003625591],"domain_scores_gemma":[0.9991925,0.000513905,0.00005946675,0.00002737471,0.000163874,0.0000428334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006765852,0.00005211151,0.0003179022,0.01691803,0.0001631005,0.0003281156,0.000115245,0.001090645,0.004562705,0.02558541,0.02953604,0.9212631],"study_design_scores_gemma":[0.000009037714,0.00006142569,0.0004610179,0.00257295,0.00009103792,0.0008946198,0.00007768609,0.0002529803,0.001354441,0.009528679,0.9846702,0.00002590123],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001223076,0.9967725,0.000683816,0.0007850501,0.0003304943,0.000004278215,0.0000265966,0.00001781489,0.00125712],"genre_scores_gemma":[0.001077177,0.9971969,0.0005026824,0.000415541,0.0001818244,0.000006645242,0.00004273008,0.00000430342,0.0005721247],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002996277,"threshold_uncertainty_score":0.01002353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03030019587493381,"score_gpt":0.3142537047785691,"score_spread":0.2839535089036353,"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."}}