{"id":"W2921281224","doi":"10.1038/s41467-019-09184-z","title":"Dissecting features of epigenetic variants underlying cardiometabolic risk using full-resolution epigenome profiling in regulatory elements","year":2019,"lang":"en","type":"article","venue":"Nature Communications","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill Genome Centre; Université Laval; McGill University; McGill University and Génome Québec Innovation Centre","funders":"University of California, San Francisco; National Institutes of Health; Vetenskapsrådet; Reumatikerförbundet; King's College London; Canadian Institutes of Health Research; National Institute for Health and Care Research; British Heart Foundation; Wellcome Trust; McGill University","keywords":"Epigenome; Epigenetics; Profiling (computer programming); Computational biology; Epigenesis; DNA methylation; Epigenomics; Biology; Genetics; Bioinformatics; Computer science; Gene; Gene expression","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002609083,0.0001657978,0.0002402218,0.0004456541,0.0001399198,0.0003252879,0.0001741454,0.0002428496,0.002027162],"category_scores_gemma":[0.0005788187,0.0001329158,0.0002103659,0.0003965173,0.0001898059,0.000118519,0.0002860515,0.0003098394,0.0002593095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001043858,"about_ca_system_score_gemma":0.0001112962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001355225,"about_ca_topic_score_gemma":0.002949554,"domain_scores_codex":[0.9998487,0.00002497565,0.000007409973,0.0000709138,0.00002534579,0.00002259631],"domain_scores_gemma":[0.9997703,0.00009585234,0.00005297994,0.00003117496,0.00002462093,0.00002493152],"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":[0.0004167888,0.00004536562,0.1132811,0.0001110225,0.0002297108,0.0001886669,0.0001919335,0.001346056,0.8656899,0.0003537397,0.0003230232,0.01782265],"study_design_scores_gemma":[0.00003606949,0.0002584911,0.8388922,0.00002513201,0.0003142662,0.001570384,0.0002453247,0.009923118,0.1432796,0.001472205,0.003958963,0.0000242188],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9840761,0.0008168159,0.01186175,0.0001121366,0.000007231634,0.00001742113,0.001952036,0.000105976,0.001050351],"genre_scores_gemma":[0.9942411,0.0001582372,0.003908507,0.00009561959,0.000008608898,0.00001822897,0.0008210585,0.00002267732,0.0007259002],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002027162,"threshold_uncertainty_score":0.006781518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0271418050460361,"score_gpt":0.3269438620247316,"score_spread":0.2998020569786955,"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."}}