{"id":"W2949023387","doi":"10.1093/bioinformatics/btz456","title":"eFORGE v2.0: updated analysis of cell type-specific signal in epigenomic data","year":2019,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":153,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Wellcome Trust; Wellcome","keywords":"Epigenomics; Computer science; SIGNAL (programming language); Type (biology); Programming language; Biology; Genetics; DNA methylation","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":[0.000230984,0.0001295788,0.0002543161,0.0001718557,0.00001688384,0.00001803486,0.0005348233,0.0001281292,0.0001292409],"category_scores_gemma":[0.000005928845,0.0001243024,0.0000719264,0.000410911,0.00002951635,0.000008927009,0.000308548,0.00006676491,0.00007256627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002067215,"about_ca_system_score_gemma":0.00007806747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001359264,"about_ca_topic_score_gemma":0.0000459352,"domain_scores_codex":[0.9990093,0.00001581148,0.0004892558,0.0001795627,0.0001069397,0.0001990985],"domain_scores_gemma":[0.9987407,0.000008291074,0.0002108343,0.0009403692,0.00005536353,0.0000444058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001961994,0.0003484086,0.06841548,0.0002755456,0.0009868811,0.000002759898,0.0007561058,0.02030622,0.8996763,0.0003286653,0.004853259,0.003854241],"study_design_scores_gemma":[0.001609776,0.0003869808,0.02408201,0.00002007122,0.0003198939,0.000003631938,0.0009070191,0.8699957,0.05755979,0.00003575807,0.04438433,0.0006950184],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950097,0.0001832739,0.001496632,0.000007058228,0.0000963611,0.0001613187,0.0002910411,0.000004963621,0.002749642],"genre_scores_gemma":[0.9848767,0.0002775987,0.009326236,0.00004818698,0.00002013447,9.752329e-7,0.005154368,0.00001383918,0.0002819774],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8496895,"threshold_uncertainty_score":0.5068904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01163571734315533,"score_gpt":0.2229129476363164,"score_spread":0.2112772302931611,"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."}}