{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0000629522,0.0004034977,0.0006637442,0.0001191999,0.0001496167,0.00007643762,0.0002506471,0.0003578423,0.00001038857],"category_scores_gemma":[0.00006704478,0.0003601674,0.0002169314,0.0001958665,0.00007375518,0.000001743634,0.0003306182,0.0001386348,0.00003033162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007580642,"about_ca_system_score_gemma":0.0005120061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001529703,"about_ca_topic_score_gemma":0.0008788038,"domain_scores_codex":[0.9984227,0.00004720353,0.0004581556,0.0006462098,0.0001077731,0.0003179272],"domain_scores_gemma":[0.9991211,0.00005271029,0.0002180466,0.0004112629,0.00007257822,0.0001243613],"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":[0.000003088307,0.000009890708,0.0001646191,0.003105037,0.0001976863,0.00001516578,0.0000296435,0.00004214782,0.0003941561,0.00002860921,0.001220151,0.9947898],"study_design_scores_gemma":[0.0001451735,0.00007940408,0.0001730558,0.002205431,0.0004175649,0.00003920259,0.00003713035,0.00007860026,0.0001073583,0.00008302925,0.9960982,0.0005358293],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.002601016,0.995304,0.0006632328,0.000008204285,0.0007510712,0.0004221976,0.00009667173,0.00001890164,0.0001347094],"genre_scores_gemma":[0.0003989794,0.9958569,0.001249818,0.00004349056,0.001557544,0.0002793152,0.0001250681,0.0001044075,0.0003844554],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9948781,"threshold_uncertainty_score":0.999885,"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."}}