{"id":"W1998811148","doi":"10.1049/iet-syb.2012.0051","title":"M‐matrix‐based stability conditions for genetic regulatory networks with time‐varying delays and noise perturbations","year":2013,"lang":"en","type":"article","venue":"IET Systems Biology","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"National Natural Science Foundation of China","keywords":"Stability (learning theory); Control theory (sociology); Gene regulatory network; Linear matrix inequality; Noise (video); Lyapunov function; Function (biology); Matrix (chemical analysis); Mathematics; Mathematical optimization; Computer science; Genetics; Gene; Physics; Biology; Nonlinear system","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":[0.0006510607,0.000968213,0.0004878968,0.0007052399,0.0003967714,0.0007521563,0.0005511041,0.0009604023,0.00251831],"category_scores_gemma":[0.002204746,0.000214201,0.0006173804,0.0003884649,0.0009253192,0.0008608039,0.0005726964,0.001021566,0.0004489804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001038185,"about_ca_system_score_gemma":0.0008229523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002544067,"about_ca_topic_score_gemma":0.002616159,"domain_scores_codex":[0.9994622,0.0001077632,0.00002551309,0.0001517926,0.000202889,0.00004981215],"domain_scores_gemma":[0.9990249,0.0004697127,0.0001923096,0.00003352382,0.0002541919,0.00002540464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001149723,0.00004684593,0.0005835852,0.0002397621,0.00004159091,0.0004374585,0.0003132023,0.7025517,0.06505232,0.2001088,0.001478541,0.02903118],"study_design_scores_gemma":[0.00001461845,0.00008604392,0.0003286049,0.00002257696,0.00001272222,0.00005533855,0.00004112565,0.9596159,0.006995272,0.03055969,0.00224906,0.00001909289],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01692627,0.0004309033,0.9753861,0.0001982472,0.00005510305,0.00005369539,0.00006370328,0.0001066793,0.006779368],"genre_scores_gemma":[0.9125727,0.001005349,0.07850099,0.0001658395,0.00009377635,0.0003106676,0.0001583774,0.00005266134,0.007139448],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002544067,"threshold_uncertainty_score":0.00842452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008427456623912281,"score_gpt":0.2279552410640889,"score_spread":0.2195277844401766,"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."}}