{"id":"W2142723924","doi":"10.1142/s0219525907000994","title":"ANALYSIS OF PREFERENTIAL NETWORK MOTIF GENERATION IN AN ARTIFICIAL REGULATORY NETWORK MODEL CREATED BY DUPLICATION AND DIVERGENCE","year":2007,"lang":"en","type":"article","venue":"Advances in Complex Systems","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Gene duplication; Functional divergence; Gene regulatory network; Divergence (linguistics); Gene; Computational biology; Topology (electrical circuits); Biology; Network topology; Transcription factor; Computer science; Genetics; Genome; Gene family; Mathematics; Gene expression; Computer network; Combinatorics","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":[0.0006078887,0.0003049844,0.0003885335,0.0007374592,0.0002971817,0.000497867,0.0006456517,0.0006438982,0.0009744063],"category_scores_gemma":[0.003697542,0.0002606833,0.0004661164,0.0003998221,0.0007033833,0.0009120239,0.0003806424,0.0003995295,0.00006151225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007573316,"about_ca_system_score_gemma":0.0003483066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001672018,"about_ca_topic_score_gemma":0.001393699,"domain_scores_codex":[0.9997311,0.0001340378,0.000008598019,0.00005370151,0.00004164419,0.0000308977],"domain_scores_gemma":[0.9978141,0.001474436,0.0003111655,0.0001305006,0.0001379297,0.000131821],"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":[0.0001072541,0.00003166589,0.002481795,0.00004851028,0.00004470353,0.0001708238,0.00005639244,0.9571424,0.006467266,0.03028523,0.0001809266,0.002983106],"study_design_scores_gemma":[0.000003652224,0.000009035076,0.0002357374,8.632251e-7,0.000003401505,0.00001628789,0.000004363784,0.9956776,0.0002350949,0.003756653,0.00005467394,0.000002542789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8209326,0.000141594,0.1766676,0.0001843127,0.00001315787,0.00002230988,0.0001449113,0.0001539784,0.001739553],"genre_scores_gemma":[0.9829727,0.00007978108,0.01625156,0.00001260136,0.000005937532,0.00003161082,0.0001291588,0.00001914311,0.0004973134],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001672018,"threshold_uncertainty_score":0.005494893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02517089690490795,"score_gpt":0.2855741052458581,"score_spread":0.2604032083409502,"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."}}