{"id":"W2796240499","doi":"10.7554/elife.32323","title":"An incoherent feedforward loop facilitates adaptive tuning of gene expression","year":2018,"lang":"en","type":"article","venue":"eLife","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute of General Medical Sciences; National Cancer Institute; National Institutes of Health; National Science Foundation","keywords":"Feed forward; Loop (graph theory); Gene expression; Feedback loop; Biology; Control theory (sociology); Computer science; Gene; Physics; Computational biology; Genetics; Mathematics; Artificial intelligence; Control (management); Control engineering; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001675726,0.0001245526,0.00015725,0.00004147859,0.00006818285,0.000007465315,0.0001838626,0.00009022104,0.00004706676],"category_scores_gemma":[0.00002450131,0.0001120012,0.00008868444,0.0000938669,0.0001245575,0.000004015845,0.00007847877,0.00003984911,0.0000177841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009899053,"about_ca_system_score_gemma":0.00004302401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001603019,"about_ca_topic_score_gemma":0.00002425943,"domain_scores_codex":[0.999076,0.00008069861,0.0001994578,0.0002907641,0.0001738653,0.0001791936],"domain_scores_gemma":[0.9991606,0.000005270366,0.00009980128,0.0004326327,0.0002015734,0.0001001225],"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":[0.00006350312,0.00003888011,0.006921141,0.000004445249,0.00006056308,4.76536e-7,0.0001232854,0.0005492356,0.9896238,0.000003666087,0.0008991995,0.001711805],"study_design_scores_gemma":[0.000189738,0.0004828651,0.004128758,0.00001289168,0.00002422244,0.000001432337,0.0002097665,0.001059428,0.9914033,0.0000110706,0.002345745,0.0001307257],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9875212,0.0005525622,0.01148795,0.00001088638,0.00006536716,0.0000728766,0.00001239942,0.00001097531,0.0002658378],"genre_scores_gemma":[0.9936706,0.00003650395,0.005522454,0.00005199091,0.0003592976,0.000007445933,0.00007209796,0.00001559066,0.0002639487],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006149528,"threshold_uncertainty_score":0.4567275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01391743292015783,"score_gpt":0.2555519233468918,"score_spread":0.2416344904267339,"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."}}