{"id":"W2004289452","doi":"10.1016/j.ymeth.2013.05.013","title":"Using evolutionary computations to understand the design and evolution of gene and cell regulatory networks","year":2013,"lang":"en","type":"review","venue":"Methods","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":47,"is_retracted":false,"has_abstract":false,"ca_institutions":"British Columbia Institute of Technology","funders":"National Institute of General Medical Sciences","keywords":"Computer science; Gene regulatory network; Context (archaeology); Systems biology; In silico; Evolutionary computation; Evolutionary algorithm; Field (mathematics); Computational biology; Theoretical computer science; Biology; Artificial intelligence; Gene; Genetics","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":[0.001320498,0.00126046,0.001456046,0.001446426,0.0003418804,0.001085806,0.002542081,0.001025517,0.001996704],"category_scores_gemma":[0.00220632,0.0006609493,0.0009412258,0.00209354,0.002156693,0.002007847,0.001001682,0.002235351,0.0009043568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00144046,"about_ca_system_score_gemma":0.001051476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002158728,"about_ca_topic_score_gemma":0.002065628,"domain_scores_codex":[0.9996654,0.00009020248,0.00002008296,0.00009488824,0.0001096581,0.00001980019],"domain_scores_gemma":[0.9992811,0.0004849443,0.00004128632,0.00006984919,0.00009723826,0.00002555044],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004874205,0.00006467139,0.0007238119,0.003640949,0.0002521549,0.00009929931,0.00009268627,0.09348653,0.006145256,0.2227632,0.00586288,0.6668199],"study_design_scores_gemma":[0.00006120598,0.0000718964,0.001241661,0.0009292925,0.0001596881,0.0005626807,0.00007854111,0.2427088,0.008996702,0.5294498,0.2156088,0.0001309169],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.004198887,0.2951334,0.6916118,0.00162287,0.000474107,0.0000528929,0.0001825239,0.0004528372,0.006270768],"genre_scores_gemma":[0.06452494,0.4970934,0.4294382,0.0006162646,0.0007876666,0.0002533896,0.0005952836,0.0002915076,0.006399325],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.002542081,"threshold_uncertainty_score":0.01045138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08751894512917092,"score_gpt":0.3688513489622446,"score_spread":0.2813324038330737,"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."}}