{"id":"W4361269820","doi":"10.1016/j.cell.2023.02.018","title":"The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models","year":2023,"lang":"en","type":"article","venue":"Cell","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"U.S. National Library of Medicine; National Human Genome Research Institute; National Institute of Mental Health; National Cancer Institute; National Science Foundation; National Institutes of Health; Generalitat de Catalunya; Ministerio de Ciencia e Innovación; Centres de Recerca de Catalunya","keywords":"Biology; Genomics; Computational biology; Genome; Personal genomics; Genetics; Genome-wide association study; Context (archaeology); Single-nucleotide polymorphism; Haplotype; Allele; Functional genomics; Gene; Genotype","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":[0.001603654,0.001423948,0.001091192,0.001251637,0.0004789681,0.001525382,0.002850914,0.001296465,0.01851482],"category_scores_gemma":[0.005179688,0.0006347465,0.001565564,0.00181512,0.000424246,0.000984725,0.001601781,0.001397393,0.007873722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006131749,"about_ca_system_score_gemma":0.001278261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004792737,"about_ca_topic_score_gemma":0.01008173,"domain_scores_codex":[0.9995964,0.000111189,0.00002579766,0.0001384362,0.00009713684,0.00003115099],"domain_scores_gemma":[0.9982805,0.0009866853,0.00008953307,0.0004374699,0.0001076152,0.00009826759],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001540079,0.0003479879,0.0402636,0.003847205,0.002253709,0.001691466,0.0003935159,0.2481292,0.02273544,0.04254744,0.5553116,0.0809388],"study_design_scores_gemma":[0.001125361,0.0002720798,0.01844039,0.0003239503,0.0005585849,0.001453497,0.0001447855,0.3950222,0.01396629,0.09792689,0.470539,0.0002270255],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.03224043,0.001630549,0.1478712,0.0009082219,0.0002336098,0.000149162,0.7662748,0.04117106,0.009520916],"genre_scores_gemma":[0.08696137,0.0009953036,0.09834901,0.0007092833,0.00008461774,0.0009229297,0.8021433,0.005640355,0.00419387],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01851482,"threshold_uncertainty_score":0.06193823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02591482944863185,"score_gpt":0.2915295470146631,"score_spread":0.2656147175660313,"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."}}