{"id":"W2068271774","doi":"10.4161/sysb.24471","title":"Long loops of information flow in genetic networks highlight an inherent directionality","year":2013,"lang":"en","type":"article","venue":"Systems Biomedicine","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Directionality; Swap (finance); Computer science; Information flow; Degree distribution; Clustering coefficient; Genetic network; Flow network; Topology (electrical circuits); Enhanced Data Rates for GSM Evolution; Cluster analysis; Mathematics; Combinatorics; Gene; Complex network; Biology; Genetics; Artificial intelligence","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":[0.0005390756,0.0003157182,0.0004427086,0.001402848,0.0005043523,0.001267082,0.0006012184,0.0007769928,0.001613395],"category_scores_gemma":[0.005590084,0.0003479682,0.0003412405,0.0008081036,0.001829518,0.002070618,0.001001306,0.0007872629,0.0002261239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006815984,"about_ca_system_score_gemma":0.0003362383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007441059,"about_ca_topic_score_gemma":0.0004239064,"domain_scores_codex":[0.9994739,0.0001157087,0.00002577644,0.0001531263,0.0001631975,0.00006836918],"domain_scores_gemma":[0.9958299,0.002530799,0.0008017716,0.0004046919,0.0002353424,0.0001975554],"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":[0.0006820993,0.0002254677,0.01443456,0.0005617304,0.0002006764,0.001115576,0.001292087,0.3757014,0.1673453,0.3488469,0.001706582,0.08788753],"study_design_scores_gemma":[0.00005905352,0.0003008051,0.01422818,0.00004989807,0.00007073041,0.0006222071,0.0001857132,0.4192066,0.03357763,0.5261123,0.005485011,0.0001017585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7043197,0.001215055,0.2851088,0.0006366247,0.00004844223,0.00008713506,0.0003285958,0.001142317,0.007113291],"genre_scores_gemma":[0.9859471,0.0003225555,0.01263279,0.00007637882,0.00002855304,0.00007545349,0.0001154546,0.00007704015,0.0007245764],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001613395,"threshold_uncertainty_score":0.005397379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005115901732318648,"score_gpt":0.2142701046591947,"score_spread":0.2091542029268761,"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."}}