{"id":"W2138288543","doi":"10.1109/aim.2008.4601795","title":"Robust stability of stochastic genetic regulatory networks with disturbance attenuation","year":2008,"lang":"en","type":"article","venue":"","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Linear matrix inequality; Stability (learning theory); Convex optimization; Mathematical optimization; Stability theory; Control theory (sociology); Computer science; Exponential stability; Regular polygon; Mathematics; Nonlinear system; Control (management); Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007032858,0.0007004204,0.0004828624,0.0002996972,0.0002217608,0.0006541681,0.0006564523,0.0004578585,0.0006476066],"category_scores_gemma":[0.002145817,0.0002100755,0.0004658745,0.0002957738,0.0007981624,0.0004785663,0.0006224978,0.0005096878,0.0001445661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009228025,"about_ca_system_score_gemma":0.0006529099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003113707,"about_ca_topic_score_gemma":0.001513976,"domain_scores_codex":[0.9993579,0.0001694196,0.0000251587,0.0001638935,0.0002183456,0.00006526161],"domain_scores_gemma":[0.9992841,0.0003326216,0.0002211686,0.00003208043,0.000111989,0.00001809237],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003581613,0.000009974104,0.0001346368,0.00003703204,0.00002368469,0.00006917652,0.00003014206,0.9437438,0.01047219,0.03724148,0.0001459052,0.008056254],"study_design_scores_gemma":[0.000005543581,0.00001618953,0.00005416045,0.000002007013,0.000004126206,0.000009337481,0.000002673986,0.9933819,0.001075587,0.005151105,0.0002935267,0.000003893384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02596945,0.0001544382,0.9711486,0.0001344319,0.00001775491,0.00001553471,0.00002986107,0.0001696214,0.002360183],"genre_scores_gemma":[0.9687964,0.0002738037,0.02844362,0.0000549277,0.00002578825,0.00009988582,0.00006808329,0.00003366519,0.002203818],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003113707,"threshold_uncertainty_score":0.00669539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01410642773755274,"score_gpt":0.1950876530379146,"score_spread":0.1809812253003619,"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."}}