{"id":"W2097626332","doi":"10.1038/nature13268","title":"Decoding the regulatory landscape of medulloblastoma using DNA methylation sequencing","year":2014,"lang":"en","type":"article","venue":"Nature","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":465,"is_retracted":false,"has_abstract":false,"ca_institutions":"SickKids Foundation; Hospital for Sick Children","funders":"","keywords":"Biology; Epigenome; Epigenetics; DNA methylation; Epigenomics; Medulloblastoma; Genetics; Genome; Chromatin; Genomics; Human genome; Computational biology; Gene; Gene expression","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":[0.0004806253,0.00008160651,0.00008606168,0.00003400075,0.00007804057,0.00001077063,0.0001145505,0.0005241121,0.000007087006],"category_scores_gemma":[0.000191049,0.00006071734,0.00005865972,0.00009059317,0.00003203797,0.000003058921,0.00004215299,0.0002630408,6.66971e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001221424,"about_ca_system_score_gemma":0.00004055884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003833422,"about_ca_topic_score_gemma":0.00001550189,"domain_scores_codex":[0.999357,0.00008783364,0.0001445724,0.0001592443,0.0001429588,0.0001083343],"domain_scores_gemma":[0.9994681,0.00003116679,0.0001181346,0.0002570708,0.00009907455,0.00002644787],"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":[0.00001085409,0.000003559767,0.002173133,0.000009896487,0.0000147295,8.999811e-8,0.00004255916,0.001335638,0.99246,0.0003467517,0.0000600069,0.003542753],"study_design_scores_gemma":[0.0001577143,0.00005148034,0.005978836,0.00001471442,0.00001988496,0.000001855486,0.00004718292,0.005435644,0.9828457,0.0005248407,0.004834351,0.00008783214],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9908582,0.002725288,0.005073743,0.00007098428,0.0001724675,0.00007099636,0.000002612247,0.00000578315,0.001019928],"genre_scores_gemma":[0.9973167,0.00004009009,0.002185817,0.0001101069,0.0002637381,0.000001463607,0.00002363387,0.00001273288,0.00004576549],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009614365,"threshold_uncertainty_score":0.4042432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01057633763773462,"score_gpt":0.2641739263958113,"score_spread":0.2535975887580766,"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."}}