{"id":"W2015789031","doi":"10.1049/ip-syb:20050006","title":"Inferring gene regulatory networks with time delays using a genetic algorithm","year":2005,"lang":"en","type":"article","venue":"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 Saskatchewan; Wilfrid Laurier University","funders":"University of Saskatchewan","keywords":"Gene regulatory network; Computer science; Bayesian network; Boolean network; Probabilistic logic; Gene; Dynamic Bayesian network; Algorithm; Gene expression; Biology; Machine learning; Genetics; Artificial intelligence; Boolean function","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002870697,0.0003266933,0.000415192,0.0001105368,0.0001489601,0.00002981632,0.000272142,0.000425101,0.00002196235],"category_scores_gemma":[0.000008035028,0.0002846915,0.0001331647,0.0002141406,0.0001604113,0.000005016543,0.0001461584,0.0001212921,0.00003449818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007087702,"about_ca_system_score_gemma":0.00009930888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006305036,"about_ca_topic_score_gemma":0.00002215286,"domain_scores_codex":[0.9979581,0.0002402842,0.0004360326,0.000657683,0.0001187642,0.000589113],"domain_scores_gemma":[0.998729,0.00001187347,0.0002268719,0.0007362611,0.0001329682,0.0001630575],"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":[0.00004182348,0.0000414297,0.007173074,0.00001145987,0.0004936954,0.00001148494,0.00001943583,0.652602,0.3214288,0.00002292778,0.0003843618,0.01776954],"study_design_scores_gemma":[0.001273824,0.0005738672,0.004971267,0.00006603535,0.0003092574,0.001039081,0.00004217726,0.9338448,0.02558542,0.00001165986,0.03113959,0.001142995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7667746,0.009266339,0.2232585,0.00001585527,0.0001815548,0.0002676841,0.00001031418,0.00004289854,0.0001822763],"genre_scores_gemma":[0.9732128,0.00008878298,0.02366994,0.00009803619,0.002182984,0.00003311079,0.000141879,0.00006776643,0.0005047061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2958434,"threshold_uncertainty_score":0.9999605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006733777485137406,"score_gpt":0.2168282927594943,"score_spread":0.2100945152743569,"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."}}