{"id":"W2754170863","doi":"10.1109/tcsii.2017.2751306","title":"M-Matrix-Based State Observer Design for Genetic Regulatory Networks With Mixed Delays","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Observer (physics); State (computer science); Mathematical optimization; Set (abstract data type); Computer science; Control theory (sociology); Separation principle; Controller (irrigation); Linear matrix inequality; Matrix (chemical analysis); Linear programming; State observer; Mathematics; Control (management); Algorithm; Artificial intelligence; Nonlinear system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008300194,0.001039258,0.0006083588,0.0003336393,0.0003563504,0.0008561204,0.000778023,0.0009175578,0.0018447],"category_scores_gemma":[0.00161465,0.0003586631,0.0005982649,0.0003295358,0.0006682529,0.0006256826,0.000807674,0.001078313,0.0003861486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008374298,"about_ca_system_score_gemma":0.0009179548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003430409,"about_ca_topic_score_gemma":0.0035819,"domain_scores_codex":[0.999478,0.0001248155,0.00003480663,0.0001509778,0.0001698854,0.00004151784],"domain_scores_gemma":[0.9993524,0.0002659833,0.0001421646,0.00003431128,0.0001855073,0.00001963507],"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":[0.0001334759,0.00004338204,0.0004916516,0.0002790234,0.00005800975,0.0001387206,0.0002062068,0.8790435,0.0235612,0.0331762,0.0008271941,0.06204158],"study_design_scores_gemma":[0.00001383408,0.00007899775,0.00006352274,0.00001136402,0.000009568364,0.00001409318,0.000007632392,0.9942616,0.001986017,0.002525527,0.001020135,0.000007755299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002658673,0.0001271942,0.9957687,0.00006035841,0.00002904179,0.00002403237,0.00001516715,0.0001105511,0.001206256],"genre_scores_gemma":[0.8051389,0.00060578,0.1887008,0.0001503385,0.00006644502,0.0005528764,0.0001234274,0.00004690416,0.004614519],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003430409,"threshold_uncertainty_score":0.006820858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02204034126126246,"score_gpt":0.2401708125280891,"score_spread":0.2181304712668267,"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."}}