{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001160448,0.0001967519,0.0001933827,0.00008541186,0.0001732312,0.00002443441,0.000314069,0.0002087396,0.0001431566],"category_scores_gemma":[0.00000644522,0.0002338334,0.00007108683,0.0001250986,0.0001012835,0.000003075638,0.0002729664,0.0002099165,0.00001309056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001105434,"about_ca_system_score_gemma":0.0001074087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001197372,"about_ca_topic_score_gemma":0.0003545866,"domain_scores_codex":[0.9986889,0.0001100221,0.0002958529,0.0005109487,0.00009881258,0.0002954115],"domain_scores_gemma":[0.9993988,0.000006510923,0.0001022932,0.0004199879,0.00002808536,0.00004433824],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003941421,0.0001482952,0.00005359664,0.000007127857,0.00002275319,0.000005042455,0.00003901445,0.002164039,0.9900154,0.0006149273,0.005897355,0.0006382347],"study_design_scores_gemma":[0.001109747,0.0003861815,0.005919692,0.000003190194,0.00001049263,0.00001777401,0.00004268288,0.00004343146,0.4171734,0.0001329277,0.5749142,0.000246324],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962704,0.0001483439,0.0001570853,0.0006748207,0.0006782533,0.0003179011,0.001689495,0.00001492281,0.00004883947],"genre_scores_gemma":[0.9886139,0.0001082064,0.001940025,0.001186095,0.0002833714,0.00003160445,0.007538423,0.0000364161,0.000261983],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5728421,"threshold_uncertainty_score":0.9535448,"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."}}