{"id":"W2780843582","doi":"10.1038/nature25169","title":"Therapeutic targeting of ependymoma as informed by oncogenic enhancer profiling","year":2017,"lang":"en","type":"article","venue":"Nature","topic":"Protein Degradation and Inhibitors","field":"Biochemistry, Genetics and Molecular Biology","cited_by":227,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University Health Centre; McMaster University; SickKids Foundation; University Health Network; University of Toronto; Ontario Institute for Cancer Research; Hospital for Sick Children","funders":"National Center for Advancing Translational Sciences; National Institute of Neurological Disorders and Stroke; National Institute of General Medical Sciences; National Institute of Diabetes and Digestive and Kidney Diseases; National Cancer Institute; National Institutes of Health","keywords":"Ependymoma; Enhancer; Profiling (computer programming); Computational biology; Biology; Cancer research; Medicine; Genetics; Pathology; Gene; Computer science; Transcription factor","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":[0.0002858868,0.0002767871,0.0002577271,0.0002524309,0.0001576638,0.0005245178,0.0002489796,0.000318591,0.00141421],"category_scores_gemma":[0.0001916329,0.0001377718,0.0001649443,0.0001443651,0.0002195758,0.0003509613,0.0002368226,0.0008727887,0.000297861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003274488,"about_ca_system_score_gemma":0.0002094964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001836551,"about_ca_topic_score_gemma":0.0005571314,"domain_scores_codex":[0.9999057,0.00001302012,0.000005281898,0.00001945446,0.00003085433,0.00002570364],"domain_scores_gemma":[0.9999471,0.00001592755,0.00001278759,0.00000956382,0.000005409766,0.000009306917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007053953,0.0001536166,0.0006033755,0.00007348421,0.00001646634,0.0004583138,0.00003317861,0.00176293,0.9609318,0.005046026,0.0005170746,0.02969823],"study_design_scores_gemma":[0.00008685967,0.0004910893,0.001669204,0.00001183586,0.00003848799,0.001271797,0.00002250914,0.006050618,0.9754393,0.001286554,0.01362163,0.00001018352],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9310822,0.004050015,0.04517272,0.0007721257,0.0001531422,0.0001824964,0.0005479046,0.0005734029,0.01746597],"genre_scores_gemma":[0.9884827,0.001231833,0.006858579,0.00007863917,0.00001417777,0.00004666269,0.0001831391,0.00004623695,0.003058242],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00141421,"threshold_uncertainty_score":0.004730999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006555520905130795,"score_gpt":0.2965779770880875,"score_spread":0.2900224561829567,"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."}}