{"id":"W2771418007","doi":"10.1186/s12918-017-0481-6","title":"Model checking optimal finite-horizon control for probabilistic gene regulatory networks","year":2017,"lang":"en","type":"article","venue":"BMC Systems Biology","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Probabilistic logic; Reachability; Computer science; Gene regulatory network; Context (archaeology); Model checking; Biological network; Computation; Optimal control; Theoretical computer science; Mathematical optimization; Algorithm; Artificial intelligence; Bioinformatics; Mathematics; Biology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008446761,0.000334444,0.0005585604,0.00005986136,0.000503348,0.0000918342,0.000667488,0.0006690389,0.000002709641],"category_scores_gemma":[0.0003838813,0.0003109896,0.0003335049,0.00003847901,0.000250936,0.000006550043,0.0001754616,0.000108135,0.0000048738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004431784,"about_ca_system_score_gemma":0.0001475738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002467216,"about_ca_topic_score_gemma":0.00006381964,"domain_scores_codex":[0.9977634,0.0001946718,0.0005180044,0.0008093241,0.00009465813,0.0006199664],"domain_scores_gemma":[0.9974666,0.00007642214,0.0005409488,0.001537903,0.0002312813,0.0001467923],"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":[0.0002383337,0.00003768795,0.009595294,0.00006491156,0.0002570463,9.812761e-7,0.000008040955,0.8767545,0.1103577,0.001321611,0.0006657467,0.0006981326],"study_design_scores_gemma":[0.001535877,0.0003915761,0.001417175,0.0000286474,0.0001844171,0.00001558533,0.0000190465,0.9903704,0.002993582,0.0001298,0.002467996,0.0004459308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3913958,0.002673297,0.6041904,0.00002342932,0.0006688825,0.0008177615,0.00005918279,0.0000302706,0.0001409862],"genre_scores_gemma":[0.9935465,0.00004197863,0.002966359,0.00003858338,0.001911553,0.0004040852,0.0002679269,0.00006116195,0.0007618195],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6021507,"threshold_uncertainty_score":0.9999342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02743609034434204,"score_gpt":0.2663482178349386,"score_spread":0.2389121274905966,"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."}}