{"id":"W2137112190","doi":"10.1093/bioinformatics/btm004","title":"SGN Sim, a Stochastic Genetic Networks Simulator","year":2007,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Set (abstract data type); Gene regulatory network; Stochastic simulation; Master regulator; Translation (biology); Stochastic modelling; Stochastic process; Simulation; Algorithm; Theoretical computer science; Gene; Transcription factor; Genetics; Mathematics; Gene expression; Programming language; Biology; Messenger RNA","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":[0.000809639,0.0007452417,0.0005749267,0.0004891679,0.0004427322,0.0006638707,0.001931121,0.0009419919,0.01242354],"category_scores_gemma":[0.00293424,0.0003721167,0.0006925856,0.0006873973,0.0004512941,0.0007594122,0.0006817949,0.000985796,0.002044941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008881345,"about_ca_system_score_gemma":0.001320871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006077577,"about_ca_topic_score_gemma":0.005320339,"domain_scores_codex":[0.9997126,0.00009701333,0.00001622643,0.00004289299,0.00009983918,0.0000313649],"domain_scores_gemma":[0.9987158,0.0007631344,0.00007811471,0.0001157252,0.000223014,0.0001042473],"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":[0.0001675906,0.00007928433,0.001862163,0.0001951102,0.00006054324,0.0001053335,0.00006133213,0.9421071,0.002254046,0.01857786,0.01381692,0.0207127],"study_design_scores_gemma":[0.00002547302,0.00001162859,0.00005259871,0.000005135285,0.000004970079,0.00001316176,0.000003029469,0.9907047,0.0007435413,0.003156791,0.005274453,0.000004556784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05540206,0.0005808136,0.8550736,0.0009914485,0.0004078619,0.0002648627,0.008730149,0.04944962,0.02909958],"genre_scores_gemma":[0.4899677,0.0008027395,0.4793264,0.0004855131,0.00008709687,0.001061927,0.01032497,0.004840065,0.01310375],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01242354,"threshold_uncertainty_score":0.04156089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006631584881200314,"score_gpt":0.2278340997158765,"score_spread":0.2212025148346762,"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."}}