{"id":"W4367049822","doi":"10.1101/2023.04.25.538222","title":"Competition between transcription and loop extrusion modulates promoter and enhancer dynamics","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Ontario Ministry of Research and Innovation; 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); Dynamics (music); Competition (biology); Loop (graph theory); Cell biology; Extrusion; Transcription factor; Biology; Genetics; Gene; Mathematics; Physics; Ecology; Materials science","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":[0.0002122762,0.0001585494,0.0002746225,0.0001132024,0.0001814736,0.0005602455,0.0003065732,0.0002469532,0.001545188],"category_scores_gemma":[0.0005380641,0.000190297,0.0001388462,0.00009303408,0.0004199801,0.0003022334,0.0004382544,0.0003727454,0.0002161814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004972455,"about_ca_system_score_gemma":0.0002592079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007794671,"about_ca_topic_score_gemma":0.0009757287,"domain_scores_codex":[0.9998612,0.00002020167,0.00000753687,0.00004209927,0.0000389715,0.00002994438],"domain_scores_gemma":[0.9997478,0.0001033626,0.0000469741,0.00002399829,0.00002268533,0.00005527737],"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":[0.0001327542,0.00001847892,0.001931251,0.00002188367,0.000008359109,0.00002988261,0.00002089547,0.004199824,0.9921611,0.0005579168,0.00004608827,0.0008716166],"study_design_scores_gemma":[0.00005415062,0.0001711855,0.01681197,0.000009115375,0.00002001463,0.0001099334,0.00009867549,0.1595622,0.8204786,0.001215648,0.001438967,0.00002945604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976709,0.00007078648,0.001761083,0.00004033262,0.000005384286,0.000002186186,0.00003031275,0.00002469469,0.000394394],"genre_scores_gemma":[0.9990801,0.00003682096,0.0006364826,0.00001006875,0.000001847713,0.000004245192,0.00004316704,0.00001388185,0.0001733418],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001545188,"threshold_uncertainty_score":0.005169153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0106676834888319,"score_gpt":0.2104964715727543,"score_spread":0.1998287880839223,"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."}}