{"id":"W3037925455","doi":"10.1016/j.clon.2020.05.022","title":"Emerging Precision Oncology Applications of Liquid Biopsy using Circulating Tumour DNA and Methylome Profiling","year":2020,"lang":"en","type":"article","venue":"Clinical Oncology","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network; University of Toronto; Ontario Institute for Cancer Research","funders":"Princess Margaret Cancer Foundation; Canadian Cancer Society Research Institute; Canadian Institutes of Health Research; Cancer Research Society; Bristol-Myers Squibb; Conquer Cancer Foundation","keywords":"Liquid biopsy; Medicine; DNA methylation; Precision oncology; Profiling (computer programming); Oncology; Biopsy; Circulating tumor DNA; Cell-free fetal DNA; DNA profiling; Precision medicine; Internal medicine; Computational biology; DNA; Cancer research; Pathology; Cancer; Gene; Genetics; Gene expression; Biology","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":[0.005042461,0.0009327884,0.001158663,0.002321954,0.000408148,0.002828142,0.001733571,0.002256093,0.004530272],"category_scores_gemma":[0.005878301,0.0005364088,0.0007762882,0.001619176,0.001580895,0.002192747,0.002165022,0.002676322,0.001211705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001160021,"about_ca_system_score_gemma":0.001264757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001600993,"about_ca_topic_score_gemma":0.003718352,"domain_scores_codex":[0.9980483,0.0007323715,0.00006208722,0.000439582,0.0005698289,0.0001478109],"domain_scores_gemma":[0.9952109,0.002589995,0.0004745273,0.0004572014,0.0009486334,0.0003186992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009727827,0.0002943372,0.04072693,0.002582811,0.0005248029,0.0006725827,0.0005433578,0.003730683,0.195702,0.03719945,0.01978288,0.6972673],"study_design_scores_gemma":[0.0003055749,0.00271446,0.05256165,0.002894203,0.00103418,0.01037624,0.001868157,0.05548176,0.2751941,0.1837822,0.4133262,0.0004612077],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.07049901,0.4290636,0.4222258,0.03809052,0.0032974,0.0004190457,0.00245374,0.002881926,0.03106891],"genre_scores_gemma":[0.5175062,0.1443657,0.2963732,0.01953818,0.004689322,0.0004807065,0.001943844,0.0003773785,0.01472541],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005042461,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07725865665633729,"score_gpt":0.4183758157014852,"score_spread":0.3411171590451479,"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."}}