{"id":"W2551476694","doi":"10.1016/j.celrep.2016.10.059","title":"eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data","year":2016,"lang":"en","type":"article","venue":"Cell Reports","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":147,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University and Génome Québec Innovation Centre; McGill Genome Centre; Centre Hospitalier Universitaire de Sherbrooke; Université de Sherbrooke","funders":"Medical Research Council; Bundesministerium für Bildung und Forschung; Cambridge BHF Centre of Research Excellence; European Commission; Engineering and Physical Sciences Research Council; National Institute for Health and Care Research; NIHR Cambridge Biomedical Research Centre; British Heart Foundation; Wellcome Trust; NHS Blood and Transplant","keywords":"Epigenomics; Computational biology; Biology; DNA methylation; Epigenome; Bioinformatics; Genetics; 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":[0.0005052019,0.000113906,0.000115282,0.00004291357,0.00004355323,0.00002405644,0.0001664486,0.0001177476,0.00004121777],"category_scores_gemma":[0.00005600151,0.00009276994,0.0000468094,0.00005502124,0.00002418896,0.000007024672,0.000155953,0.00003550007,0.00001198105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000219286,"about_ca_system_score_gemma":0.00008928951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007453294,"about_ca_topic_score_gemma":0.00001514976,"domain_scores_codex":[0.9987888,0.00002506428,0.0003551244,0.0005141341,0.00009004995,0.0002267718],"domain_scores_gemma":[0.9989216,0.00002419785,0.0001650682,0.0007770607,0.0000644135,0.00004771492],"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":[0.00002851082,0.00003811651,0.006408032,0.00002141326,0.000004081312,0.00002169413,0.00001244669,0.00001839005,0.9863328,0.000003224122,0.001194196,0.00591708],"study_design_scores_gemma":[0.0002972883,0.00006785189,0.001655767,0.00001168779,0.000006812086,0.000004297487,0.000007732968,0.00002117733,0.8262218,0.0003630603,0.1711935,0.0001491355],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9648031,0.004667002,0.02860762,0.00002617417,0.0004190606,0.0003175595,0.00002242506,0.000007582816,0.00112948],"genre_scores_gemma":[0.9936098,0.0008009924,0.002380113,0.00001637356,0.0002371596,0.00001523278,0.0002992901,0.00002778473,0.002613298],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1699993,"threshold_uncertainty_score":0.3783048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04356592354768934,"score_gpt":0.2886311607982309,"score_spread":0.2450652372505416,"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."}}