{"id":"W3128912337","doi":"10.1111/febs.15750","title":"Clinical advances in targeting epigenetics for cancer therapy","year":2021,"lang":"en","type":"review","venue":"FEBS Journal","topic":"Protein Degradation and Inhibitors","field":"Biochemistry, Genetics and Molecular Biology","cited_by":101,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"Canadian Institutes of Health Research; Ontario Institute for Cancer Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Epigenetics; Cancer therapy; Epigenetic therapy; Cancer; Medicine; Computational biology; Biology; Bioinformatics; DNA methylation; Internal medicine; Genetics","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":[0.001025262,0.0008926165,0.001084696,0.001843497,0.0003021219,0.001056931,0.0008632549,0.001501532,0.006232842],"category_scores_gemma":[0.0009373727,0.0002492972,0.0007357625,0.001316691,0.0005777387,0.001303709,0.0008873081,0.003306061,0.003714174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008034808,"about_ca_system_score_gemma":0.0009916079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005937076,"about_ca_topic_score_gemma":0.001293317,"domain_scores_codex":[0.9997562,0.00005445032,0.00002877864,0.00004098524,0.00009339587,0.00002628133],"domain_scores_gemma":[0.9995705,0.0002091425,0.00004184067,0.0000170769,0.0001160577,0.00004537965],"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":[0.0001016084,0.000099267,0.0001094454,0.009836444,0.00007244971,0.0002305624,0.00004336472,0.0004253141,0.003410572,0.007092382,0.04354529,0.9350333],"study_design_scores_gemma":[0.00002749175,0.0001121515,0.0003983161,0.001930276,0.00006640951,0.0008178931,0.0000244488,0.000104793,0.0007037565,0.002472213,0.9933271,0.00001515503],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000102911,0.9967269,0.0003128992,0.0007929046,0.0004020252,0.000007886452,0.00002134286,0.00001596761,0.00161721],"genre_scores_gemma":[0.000731219,0.9969772,0.0004344996,0.0005046125,0.0003455192,0.00001166638,0.00003811948,0.000003365308,0.0009538567],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.006232842,"threshold_uncertainty_score":0.0208509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0727116662767061,"score_gpt":0.4457635415172905,"score_spread":0.3730518752405844,"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."}}