{"id":"W4407824354","doi":"10.1002/9781394191369.ch7.2","title":"Cancer Genomics, Biomarkers and Precision Medicine","year":2025,"lang":"en","type":"other","venue":"","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network; University of Toronto; Ontario Institute for Cancer Research","funders":"","keywords":"Precision medicine; Genomics; Computational biology; Cancer Medicine; Medicine; Data science; Cancer; Biology; Computer science; Internal medicine; Genetics; Genome; Gene","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":[0.003558261,0.000722405,0.0007647328,0.002495361,0.001214502,0.005210394,0.0009548191,0.003952686,0.04457249],"category_scores_gemma":[0.008051032,0.0002853794,0.0004923168,0.00225178,0.003142204,0.003269884,0.003241543,0.004284862,0.01670612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003003228,"about_ca_system_score_gemma":0.004336545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004130756,"about_ca_topic_score_gemma":0.005710067,"domain_scores_codex":[0.9978623,0.0009231392,0.000109393,0.0002226817,0.0007248515,0.0001575961],"domain_scores_gemma":[0.996727,0.001717718,0.0002067463,0.0002704503,0.0006639952,0.0004140907],"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":[0.00003818836,0.00003386503,0.0005852623,0.0007238309,0.00002544599,0.0001982491,0.0002324436,0.000358659,0.0004157729,0.1989146,0.3437761,0.4546977],"study_design_scores_gemma":[0.000004224265,0.00001025276,0.0002089722,0.0005378577,0.00000362915,0.0002037503,0.00006561826,0.00006538806,0.0001146738,0.04133596,0.9574409,0.000008716213],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"other","genre_scores_codex":[0.001067446,0.3923025,0.013365,0.227886,0.01285406,0.00009440212,0.0009691205,0.0007244256,0.3507371],"genre_scores_gemma":[0.03249655,0.5779455,0.02357554,0.08370759,0.01875752,0.0002903909,0.00157673,0.0005305341,0.2611196],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.04457249,"threshold_uncertainty_score":0.1491099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006333243723011878,"score_gpt":0.2624848005457404,"score_spread":0.2561515568227285,"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."}}