{"id":"W4392563655","doi":"10.1093/bioinformatics/btae136","title":"EpiVar Browser: advanced exploration of epigenomics data under controlled access","year":2024,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Institute of General Medical Sciences; Genome Canada; Canadian Institutes of Health Research; Alliance de recherche numérique du Canada; Ministry of Education, Culture, Sports, Science and Technology; National Institute of Diabetes and Digestive and Kidney Diseases; McGill University","keywords":"Computer science; Epigenomics; Software; Data exploration; World Wide Web; Programming language; Data mining; Biology; Visualization","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003468792,0.0001285146,0.0001893128,0.00007188261,0.00004338997,0.00009056491,0.0004061146,0.0001162335,0.00001294071],"category_scores_gemma":[0.0001091915,0.0001124422,0.00006591802,0.0001207482,0.00004278174,0.00007883684,0.0002692932,0.00005738913,0.00001638949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001247057,"about_ca_system_score_gemma":0.0001288516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004942452,"about_ca_topic_score_gemma":0.00001450642,"domain_scores_codex":[0.9990106,0.00002588717,0.0004947264,0.0001773878,0.0001442122,0.0001472242],"domain_scores_gemma":[0.9990364,0.00003626177,0.0001453399,0.0006414473,0.00009093127,0.000049585],"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":[0.001183057,0.0002726167,0.0006011248,0.001498878,0.000948534,0.000004227105,0.001377578,0.03569929,0.6778977,0.01109213,0.004840307,0.2645846],"study_design_scores_gemma":[0.004753141,0.0006379113,0.0007420773,0.0001736262,0.0002348449,0.000004388418,0.0008170784,0.2902926,0.5421879,0.00624423,0.1530708,0.0008413373],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3189432,0.009225083,0.6634558,0.0003777742,0.0009194296,0.0008767626,0.0003066975,0.00005603921,0.00583923],"genre_scores_gemma":[0.9804466,0.003607523,0.01373583,0.00009470363,0.0001726744,0.00001718563,0.001610296,0.00002669567,0.000288457],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6615034,"threshold_uncertainty_score":0.4585261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06283079410161505,"score_gpt":0.339123684800046,"score_spread":0.276292890698431,"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."}}