{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003734547,0.0001043645,0.00009544035,0.00003130436,0.0001067545,0.00001816468,0.0001686349,0.0001126891,0.00001824439],"category_scores_gemma":[0.00006520569,0.00007735206,0.00008738069,0.000108068,0.00005545632,0.000001798431,0.00008789344,0.00006224389,0.00003636758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009023759,"about_ca_system_score_gemma":0.00005021956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003465286,"about_ca_topic_score_gemma":0.00002817984,"domain_scores_codex":[0.9992244,0.00008734747,0.0001600353,0.0001980092,0.0001153025,0.0002149697],"domain_scores_gemma":[0.9994612,0.00007446334,0.00007177785,0.0002802351,0.00005222036,0.00006008569],"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":[0.00002700726,0.0000343291,0.0002599756,0.00001476762,0.00002699408,0.000001727249,0.0005506332,0.007210322,0.9873825,0.00002158995,0.0009457343,0.003524468],"study_design_scores_gemma":[0.0005453594,0.000203817,0.00326389,0.000007334521,0.00002260329,0.000001448931,0.0002582999,0.009022266,0.7263376,0.0005432502,0.2595616,0.0002324947],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9816169,0.006138205,0.009233749,0.0001049687,0.0001193999,0.0001553674,0.00004618762,0.00001461723,0.002570581],"genre_scores_gemma":[0.9893839,0.001314216,0.0005316914,0.00001521037,0.00015526,0.000007128473,0.0001564222,0.00002150638,0.00841471],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2610448,"threshold_uncertainty_score":0.3154325,"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."}}