{"id":"W1978923963","doi":"10.1371/journal.pcbi.1002734","title":"Regulatory Network Structure as a Dominant Determinant of Transcription Factor Evolutionary Rate","year":2012,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Fungal and yeast genetics research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Biology; Transcription factor; Gene regulatory network; Evolutionary dynamics; Evolutionary biology; Human evolutionary genetics; Genetics; Gene; Niche; Computational biology; Adaptation (eye); Rate of evolution; Interaction network; Phylogenetics; Gene expression; Ecology; Population; Neuroscience","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001076938,0.0001334704,0.0001617487,0.00004837561,0.00007588875,0.00000405259,0.0001384112,0.0001820242,0.000102916],"category_scores_gemma":[0.00004648422,0.0001133469,0.00008097989,0.00008411985,0.0001588626,0.000005183552,0.0000711309,0.0000916119,0.00001621937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001636087,"about_ca_system_score_gemma":0.0001097311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005872715,"about_ca_topic_score_gemma":0.000003251874,"domain_scores_codex":[0.9989154,0.0001751181,0.0002327738,0.0002233448,0.0001249096,0.0003284084],"domain_scores_gemma":[0.999461,0.0000457717,0.00008654646,0.0001469691,0.000158427,0.0001013151],"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":[0.0002060879,0.00008598228,0.01561562,0.00002788442,0.00007314132,5.862008e-7,0.00004575512,0.002483891,0.979054,0.00114183,0.0003371321,0.0009280491],"study_design_scores_gemma":[0.00104746,0.001000925,0.3012093,0.00003659691,0.00003812319,0.00007634646,0.00002781652,0.003463511,0.6772147,0.007888785,0.007581817,0.0004146166],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958512,0.002037839,0.001368608,0.00007422607,0.0002397144,0.0001680723,0.000148001,0.000005938924,0.0001064115],"genre_scores_gemma":[0.996335,0.0000502947,0.002345265,0.0001040492,0.0004872966,0.00001184454,0.0005461831,0.0000140632,0.0001060262],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3018393,"threshold_uncertainty_score":0.4622151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01642552579309988,"score_gpt":0.2705917650648714,"score_spread":0.2541662392717715,"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."}}