{"id":"W2789727733","doi":"10.1038/nrclinonc.2018.30","title":"Epigenome-based cancer risk prediction: rationale, opportunities and challenges","year":2018,"lang":"en","type":"review","venue":"Nature Reviews Clinical Oncology","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":173,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; McGill University and Génome Québec Innovation Centre","funders":"National Institute for Health and Care Research","keywords":"Epigenome; Epigenomics; DNA methylation; Medicine; Risk analysis (engineering); Cancer; Epigenetics; Bioinformatics; Biology; Genetics; Internal medicine","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":[0.004695185,0.00108421,0.002712418,0.001640071,0.0002989988,0.002010886,0.002101096,0.002029586,0.002028927],"category_scores_gemma":[0.007805557,0.0004742992,0.0008291403,0.001321131,0.001309887,0.001977149,0.00128195,0.003845611,0.0009303139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001149636,"about_ca_system_score_gemma":0.002052055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002189804,"about_ca_topic_score_gemma":0.003603315,"domain_scores_codex":[0.9989462,0.0003876464,0.00009433761,0.0002533754,0.0002598386,0.00005860763],"domain_scores_gemma":[0.9945171,0.004098336,0.0002444857,0.0001383856,0.0008665521,0.0001351015],"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":[0.0001321212,0.00005457333,0.002867869,0.006018154,0.0004221839,0.0001826133,0.00008090947,0.001320613,0.001068534,0.0142706,0.02192292,0.951659],"study_design_scores_gemma":[0.00008849267,0.0002573905,0.006728067,0.01352741,0.001403605,0.003111403,0.0003114278,0.004371972,0.002469739,0.05985036,0.9077077,0.0001724528],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002652037,0.9928057,0.002366303,0.003489467,0.0003229812,0.000007265948,0.00008513979,0.00001851093,0.000639591],"genre_scores_gemma":[0.007211964,0.9848905,0.003387935,0.00233439,0.001401389,0.00002823438,0.0001712581,0.000009774034,0.0005645696],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004695185,"threshold_uncertainty_score":0.02483082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3237319021419476,"score_gpt":0.4827693415364815,"score_spread":0.1590374393945339,"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."}}