{"id":"W2060977279","doi":"10.1103/physreve.73.031912","title":"Network growth models and genetic regulatory networks","year":2006,"lang":"en","type":"article","venue":"Physical Review E","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Science Foundation","keywords":"Counterintuitive; Degree distribution; Node (physics); Computer science; Scaling; Degree (music); Gene regulatory network; Gene; Class (philosophy); Genome; Computational biology; Biology; Genetics; Mathematics; Complex network; Physics; Artificial intelligence; Gene expression","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":[0.001090853,0.001013432,0.0006212054,0.0009616448,0.0005776974,0.001363957,0.001211459,0.001950209,0.002109062],"category_scores_gemma":[0.008487077,0.0003606803,0.0006475766,0.001293375,0.001744854,0.002056475,0.0006167103,0.001317256,0.0004353821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001648687,"about_ca_system_score_gemma":0.0005829675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003339058,"about_ca_topic_score_gemma":0.002225052,"domain_scores_codex":[0.9995384,0.0002219902,0.00001326132,0.00007265522,0.00009867831,0.00005506866],"domain_scores_gemma":[0.9967461,0.002546064,0.0003136742,0.0001090343,0.0001712143,0.0001138733],"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":[0.00001777706,0.00001674657,0.0004917521,0.00005170753,0.00002326806,0.00006844148,0.00007510476,0.6381834,0.0009112051,0.3549257,0.001186568,0.004048396],"study_design_scores_gemma":[0.00001151642,0.0000112629,0.0001134868,0.00001104931,0.000006709698,0.00002995243,0.0000187648,0.770299,0.0001912751,0.2268165,0.002482384,0.000008057968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.107763,0.004441357,0.8583696,0.003910191,0.0002030165,0.00006446473,0.000287938,0.0004613398,0.02449904],"genre_scores_gemma":[0.9119613,0.005295801,0.07023839,0.0004217919,0.0003059277,0.0002241088,0.0003617069,0.0001722691,0.01101869],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003339058,"threshold_uncertainty_score":0.01196218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008240815115916456,"score_gpt":0.2353828153358101,"score_spread":0.2271420002198936,"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."}}