{"id":"W2061854785","doi":"10.1038/srep04819","title":"Transittability of complex networks and its applications to regulatory biomolecular networks","year":2014,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Program for New Century Excellent Talents in University; Japan Society for the Promotion of Science; Council for Science and Technology Policy; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Controllability; Computer science; Complex network; Graph; Set (abstract data type); Kernel (algebra); Complex system; State (computer science); Biological network; Topology (electrical circuits); Theoretical computer science; Distributed computing; Algorithm; Mathematics; Artificial intelligence; Bioinformatics; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001006272,0.0004865676,0.0003537791,0.001344944,0.0004040044,0.0007292889,0.0006345885,0.0004200135,0.002275869],"category_scores_gemma":[0.005379195,0.0003455356,0.0007748787,0.0006423251,0.001658266,0.001589432,0.0009904918,0.001195897,0.0002304964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001137999,"about_ca_system_score_gemma":0.0006189476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002348113,"about_ca_topic_score_gemma":0.001367688,"domain_scores_codex":[0.9995564,0.0001257788,0.0000309403,0.0001468495,0.00008838786,0.00005156027],"domain_scores_gemma":[0.9962219,0.002295034,0.0004906462,0.0004300156,0.0003497649,0.0002127079],"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":[0.0002998718,0.0001021328,0.003862026,0.0001913909,0.00008956123,0.0002437501,0.0003940752,0.5311898,0.04016097,0.3601863,0.000672949,0.06260712],"study_design_scores_gemma":[0.00001358902,0.00005451207,0.0006190579,0.00000897128,0.00001427045,0.0000568435,0.00003672799,0.8458697,0.006019884,0.1464051,0.0008855025,0.00001589569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07765834,0.0001977227,0.9194787,0.0001665226,0.000009339345,0.00004756981,0.00008580655,0.0005503516,0.001805675],"genre_scores_gemma":[0.8879695,0.0003419995,0.1097674,0.00004864821,0.00002205071,0.000102337,0.0001981706,0.0001070491,0.001442902],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002348113,"threshold_uncertainty_score":0.008256793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009837347896152477,"score_gpt":0.2399736184766629,"score_spread":0.2301362705805104,"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."}}