{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00862005,0.002664061,0.001990499,0.007828668,0.001048985,0.003096115,0.002413162,0.001965252,0.03640658],"category_scores_gemma":[0.02425472,0.001757462,0.002318181,0.00495298,0.0008528613,0.003265596,0.004329198,0.003001905,0.01122106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008690957,"about_ca_system_score_gemma":0.001969733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001777393,"about_ca_topic_score_gemma":0.004048052,"domain_scores_codex":[0.9972669,0.0007864027,0.0003867738,0.0007935174,0.000599591,0.0001667828],"domain_scores_gemma":[0.9882978,0.008846974,0.0007294015,0.001363704,0.0004542394,0.0003079091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003246167,0.0004282178,0.03232064,0.00692684,0.00228387,0.003911228,0.002325574,0.01547709,0.05462481,0.03292257,0.5232974,0.3222356],"study_design_scores_gemma":[0.001335651,0.0003460843,0.02642914,0.001069009,0.0006352505,0.004161994,0.0005582229,0.1485117,0.06571292,0.125834,0.6247573,0.0006487416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007631271,0.0007811399,0.5144576,0.0005428635,0.000258171,0.0003381657,0.1716555,0.3011655,0.003169801],"genre_scores_gemma":[0.05523004,0.0009568112,0.7110347,0.0009708917,0.0001962003,0.002437243,0.1914156,0.0341306,0.003627836],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03640658,"threshold_uncertainty_score":0.1217921,"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."}}