{"id":"W1943756471","doi":"10.1038/nm.3915","title":"Toward understanding and exploiting tumor heterogeneity","year":2015,"lang":"en","type":"article","venue":"Nature Medicine","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":769,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre","funders":"Rosetrees Trust; National Cancer Institute; National Institute for Health and Care Research; Cancer Research UK; Francis Crick Institute","keywords":"Tumor heterogeneity; Genetic heterogeneity; Perspective (graphical); Epigenetics; Precision medicine; Theme (computing); Data science; Engineering ethics; Computational biology; Biology; Computer science; Genetics; Cancer; Phenotype; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001167147,0.000389542,0.0006986987,0.001090416,0.0003060096,0.002087918,0.0007287459,0.0006439046,0.0007548691],"category_scores_gemma":[0.002167986,0.0003314145,0.0004087043,0.000789525,0.001095089,0.004290662,0.001699723,0.001678184,0.0002209339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007151156,"about_ca_system_score_gemma":0.0008890396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008942028,"about_ca_topic_score_gemma":0.0009306751,"domain_scores_codex":[0.9997187,0.00007317906,0.00001490235,0.00007482035,0.00007790747,0.00004038519],"domain_scores_gemma":[0.9989098,0.0004824535,0.0001846954,0.0002597288,0.0000954457,0.00006777977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002571107,0.0001476957,0.04439498,0.0007174526,0.0002351964,0.0005660117,0.0008988056,0.02478584,0.2776587,0.2449749,0.003800659,0.4015625],"study_design_scores_gemma":[0.00003556189,0.0001612851,0.01754218,0.0001098014,0.0002846348,0.001887214,0.001081095,0.1393801,0.1283519,0.6622412,0.04882975,0.00009523774],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2365898,0.01438994,0.7338966,0.006726152,0.0001252409,0.00007313602,0.0005741336,0.0008645702,0.006760574],"genre_scores_gemma":[0.8673227,0.009079391,0.1204887,0.001020341,0.0002473301,0.00005916662,0.0002704923,0.0001365056,0.001375372],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002087918,"threshold_uncertainty_score":0.006172538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06055799054784175,"score_gpt":0.3030918968717587,"score_spread":0.2425339063239169,"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."}}