{"id":"W4385878117","doi":"10.21203/rs.3.rs-3164817/v1","title":"Competition between transcription and loop extrusion modulates promoter and enhancer dynamics","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Ontario Ministry of Research and Innovation; Biotechnology and Biological Sciences Research Council; Institute of Genetics; National Institutes of Health; Oncode Institute; Laboratoire d'Excellence INRT; Canadian Institutes of Health Research; KWF Kankerbestrijding; Ligue Contre le Cancer; Fondation ARC pour la Recherche sur le Cancer; Agence Nationale de la Recherche; Ministerie van Volksgezondheid, Welzijn en Sport; Deutsche Forschungsgemeinschaft; Academy of Medical Sciences","keywords":"Enhancer; Transcription (linguistics); Competition (biology); Dynamics (music); Loop (graph theory); Extrusion; Transcription factor; Cell biology; Biology; Genetics; Physics; Gene; Mathematics; Materials science; Combinatorics","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":[0.0007680272,0.0003120652,0.0007263398,0.0002514319,0.000400656,0.001618108,0.0008092302,0.0008854534,0.008082517],"category_scores_gemma":[0.002393074,0.0005088237,0.000279191,0.000237881,0.0007006243,0.001653143,0.0008935252,0.0008676808,0.00113208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006285557,"about_ca_system_score_gemma":0.0003381279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003232688,"about_ca_topic_score_gemma":0.0005726631,"domain_scores_codex":[0.9994116,0.00008168978,0.00002605701,0.0002116698,0.0001151985,0.0001538148],"domain_scores_gemma":[0.9979942,0.001318742,0.0001813912,0.000129411,0.0001127977,0.0002635188],"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":[0.000659493,0.00005848557,0.0008379609,0.00005024142,0.000007759278,0.00005127369,0.00003579924,0.001092679,0.9896355,0.003423559,0.0001452671,0.004001949],"study_design_scores_gemma":[0.0001747607,0.0002339317,0.007704186,0.0000111635,0.00002721515,0.0001755604,0.0001147268,0.04463447,0.941653,0.003838934,0.00139431,0.00003777574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9863506,0.0005247415,0.009563414,0.000284996,0.00006383892,0.000009058164,0.00008512137,0.0001714492,0.00294669],"genre_scores_gemma":[0.997335,0.00008993523,0.001276073,0.00004552144,0.00002112396,0.000009604928,0.00006859218,0.00008724914,0.001066873],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008082517,"threshold_uncertainty_score":0.02703869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03872341218168025,"score_gpt":0.3369502924342033,"score_spread":0.2982268802525231,"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."}}