{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001052482,0.00008954428,0.00008777288,0.00001502746,0.0001268829,0.00002640606,0.0002617265,0.0006251403,0.00004212068],"category_scores_gemma":[0.0003018029,0.0000758,0.00006549457,0.00002031315,0.00005223879,0.00000839578,0.00007327625,0.000362155,0.000008759889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008399013,"about_ca_system_score_gemma":0.0000917887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007795601,"about_ca_topic_score_gemma":0.000004124799,"domain_scores_codex":[0.9994416,0.00001467751,0.0001343393,0.0001554828,0.0001239937,0.0001299527],"domain_scores_gemma":[0.9993835,0.000005182606,0.0001780197,0.0003159149,0.00008234425,0.00003503767],"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":[0.00003813603,0.00001467753,0.0005775566,0.00001603927,0.00002752835,6.17558e-7,0.00001473073,4.163654e-7,0.9889259,0.0001338244,0.005889254,0.004361276],"study_design_scores_gemma":[0.0002706373,0.00007419801,0.0002771434,0.00001263484,0.000005787998,0.000002963327,0.00002474493,0.000007543646,0.9330356,0.00008155374,0.06611837,0.00008878715],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9851592,0.005437352,0.0001041241,0.000364256,0.0003015904,0.0001916831,0.00001452901,0.000009362639,0.008417886],"genre_scores_gemma":[0.9969714,0.0001264982,0.0004608037,0.0004244815,0.0001690838,0.00001067385,0.0001190203,0.00001113252,0.001706937],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06022912,"threshold_uncertainty_score":0.4821654,"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."}}