{"id":"W4281974954","doi":"10.1101/2022.06.01.494187","title":"The kinetic landscape of human transcription factors","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canadian Institutes of Health Research; National Cancer Institute; Melanoma Research Alliance; National Institutes of Health; National Science Foundation","keywords":"Bursting; Transcription factor; Biology; Chromatin; Transcription (linguistics); Genetics; Gene; Computational biology; Gene regulatory network; Gene expression; Neuroscience","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005135146,0.0004195925,0.0004154355,0.0001257581,0.0003583166,0.00007169428,0.0008006035,0.000372205,0.00008678493],"category_scores_gemma":[0.00005169469,0.0003719159,0.000400435,0.0003066704,0.0001553372,0.000003450262,0.0004754214,0.0004141254,0.000002754084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005709601,"about_ca_system_score_gemma":0.0002239389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003265083,"about_ca_topic_score_gemma":0.00001296125,"domain_scores_codex":[0.9976871,0.0002482035,0.0005318911,0.0007442612,0.0003858064,0.0004027496],"domain_scores_gemma":[0.9975764,0.00001961216,0.0004514052,0.001592289,0.0002303525,0.0001299852],"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":[0.00002059609,0.00005952812,0.02150201,0.00008152891,0.000339906,0.00000220152,0.000006174723,0.0009594497,0.9763438,0.0001190593,0.0005648148,9.444324e-7],"study_design_scores_gemma":[0.0003996518,0.0001974429,0.1797386,0.00004677718,0.0003807309,8.523902e-9,0.00002027641,0.0001615525,0.7926759,0.00000315727,0.02569486,0.0006810829],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9940266,0.004660663,0.0001996536,0.00004937096,0.000547145,0.0003431258,0.000109565,0.00004589939,0.00001799392],"genre_scores_gemma":[0.9986891,0.0005737721,0.0001379666,0.00002637481,0.0003313626,0.0001043421,0.000005981455,0.00009685691,0.0000342766],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1836679,"threshold_uncertainty_score":0.9998733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01111731535938845,"score_gpt":0.2178620195931525,"score_spread":0.206744704233764,"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."}}