{"id":"W3045910546","doi":"10.1038/s41586-020-2493-4","title":"Expanded encyclopaedias of DNA elements in the human and mouse genomes","year":2020,"lang":"en","type":"article","venue":"Nature","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2581,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; McGill University; Simon Fraser University; Université de Montréal; Montreal Clinical Research Institute","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Institute of General Medical Sciences; National Institutes of Health; National Cancer Institute; National Human Genome Research Institute; Biotechnology and Biological Sciences Research Council","keywords":"ENCODE; Genome; Chromatin; Computational biology; Biology; Human genome; Epigenomics; Context (archaeology); Genetics; DNA methylation; Gene; DNA; Transcription (linguistics); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001408316,0.001135592,0.001126574,0.01389598,0.000589866,0.001682689,0.001115135,0.0006206044,0.03749512],"category_scores_gemma":[0.003580666,0.0006298925,0.0006317132,0.01860645,0.000353082,0.001158336,0.0015889,0.001073916,0.01716813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005227351,"about_ca_system_score_gemma":0.001441882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004206598,"about_ca_topic_score_gemma":0.006894152,"domain_scores_codex":[0.9988242,0.0001792613,0.0003037348,0.0003246474,0.0002796826,0.00008841557],"domain_scores_gemma":[0.9974625,0.0009202985,0.0005008704,0.0004522834,0.0003150922,0.000348954],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001873471,0.0002379132,0.02130111,0.007418374,0.0003181354,0.001335582,0.001041613,0.002503881,0.06802804,0.0183356,0.5909525,0.2866537],"study_design_scores_gemma":[0.00009485769,0.00006434181,0.01969528,0.0004343442,0.0001037509,0.0007853446,0.0001158969,0.0003869559,0.007269848,0.002463464,0.9685342,0.00005175321],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.00715289,0.002433421,0.006241987,0.0002467289,0.000161965,0.0001253756,0.9733862,0.002922282,0.007329189],"genre_scores_gemma":[0.008471903,0.002750229,0.02149712,0.000211714,0.00009115705,0.0003322876,0.9630126,0.0006916313,0.002941395],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03749512,"threshold_uncertainty_score":0.1254336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006242303003780561,"score_gpt":0.2383427587458579,"score_spread":0.2321004557420774,"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."}}