{"id":"W4280626237","doi":"10.1099/mgen.0.000833","title":"RegulonDB 11.0: Comprehensive high-throughput datasets on transcriptional regulation in Escherichia coli K-12","year":2022,"lang":"en","type":"article","venue":"Microbial Genomics","topic":"Bacterial Genetics and Biotechnology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":117,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; Dirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México; National Institutes of Health; Universidad Nacional Autónoma de México; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Operon; Throughput; Biology; Computational biology; Transcriptional regulation; Variety (cybernetics); Regulation of gene expression; Gene; Genomics; Set (abstract data type); Resource (disambiguation); Transcription (linguistics); Transcription factor; Escherichia coli; Data science; Computer science; Genetics; Genome; Computer network; Artificial intelligence","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":[0.002227506,0.002447722,0.002215292,0.004110486,0.001345136,0.00199339,0.002549593,0.001916955,0.005357449],"category_scores_gemma":[0.004236034,0.001096953,0.002280599,0.005471615,0.0006479576,0.001275253,0.002762451,0.002158175,0.007458747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001295914,"about_ca_system_score_gemma":0.003153456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007498917,"about_ca_topic_score_gemma":0.008604818,"domain_scores_codex":[0.9970891,0.0005360001,0.000444926,0.0008964656,0.0006782687,0.0003553506],"domain_scores_gemma":[0.9981087,0.0004733136,0.0002759269,0.0004989305,0.0004339772,0.0002091253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.005859538,0.001075414,0.06429657,0.0226426,0.002392326,0.001022359,0.001058876,0.0350557,0.1362188,0.00947308,0.6316396,0.08926506],"study_design_scores_gemma":[0.0008984493,0.0006616631,0.112651,0.001398913,0.0008770548,0.000686937,0.0005245553,0.0144013,0.04564456,0.008594371,0.8130636,0.000597645],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01626274,0.001233012,0.00560327,0.0001653564,0.00007879489,0.0001381108,0.9675298,0.007304547,0.00168436],"genre_scores_gemma":[0.007027149,0.0003774723,0.006712122,0.00008248688,0.000008741058,0.0003738132,0.9845461,0.0004746498,0.0003976053],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.007498917,"threshold_uncertainty_score":0.01792246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01168553392167159,"score_gpt":0.2135797308728682,"score_spread":0.2018941969511966,"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."}}