{"id":"W2054854610","doi":"10.1155/2014/761562","title":"State Observer Design for Delayed Genetic Regulatory Networks","year":2014,"lang":"en","type":"article","venue":"Computational and Mathematical Methods in Medicine","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Beijing Wuzi University","keywords":"Observer (physics); Gene regulatory network; Computer science; Linear matrix inequality; State (computer science); Control theory (sociology); Genetic network; Mathematical optimization; Gene; Genetics; Biology; Mathematics; Control (management); Artificial intelligence; Gene expression; Physics","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":[0.001039171,0.001083948,0.0009854138,0.0003825225,0.0003640698,0.001194515,0.0009936355,0.001131376,0.002020281],"category_scores_gemma":[0.002454103,0.0003485637,0.0005037113,0.0003708821,0.0009026625,0.000650305,0.0008551804,0.001329493,0.0003041762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001252067,"about_ca_system_score_gemma":0.001113255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005910066,"about_ca_topic_score_gemma":0.003948504,"domain_scores_codex":[0.999254,0.000164556,0.00003889976,0.0002523604,0.0002078402,0.00008217913],"domain_scores_gemma":[0.998868,0.0005105158,0.0001969892,0.0000462396,0.0003453274,0.00003298348],"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":[0.0001268548,0.00004053487,0.0004313716,0.0002024349,0.00003732791,0.0001166064,0.0001642057,0.9383361,0.01002273,0.02045365,0.0006436128,0.02942452],"study_design_scores_gemma":[0.00001677207,0.00005368275,0.00007149381,0.000007576494,0.000007986408,0.000009236149,0.00000578408,0.9963168,0.0009815303,0.001929629,0.0005929309,0.000006616925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006026592,0.0001941972,0.9913839,0.0001131684,0.00005210323,0.00003735794,0.00003489795,0.0001861964,0.001971636],"genre_scores_gemma":[0.9314237,0.0004772808,0.06145605,0.0001195212,0.00005384911,0.0004369182,0.0001783723,0.00003489069,0.005819396],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005910066,"threshold_uncertainty_score":0.01175135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0400740962968691,"score_gpt":0.3536375812136368,"score_spread":0.3135634849167677,"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."}}