{"id":"W2151484461","doi":"10.1186/1752-0509-8-60","title":"Gene perturbation and intervention in context-sensitive stochastic Boolean networks","year":2014,"lang":"en","type":"article","venue":"BMC Systems Biology","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Systems biology; Gene regulatory network; Perturbation (astronomy); Computational biology; Context (archaeology); Computer science; Theoretical computer science; Biological network; Boolean network; Intervention (counseling); Biology; Statistical physics; Mathematics; Gene; Psychology; Genetics; Algorithm; Physics; Boolean function; Gene expression","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":[0.0005928967,0.0004698018,0.0005614964,0.0003860135,0.0004370995,0.0006626485,0.0009525338,0.0007176217,0.002475964],"category_scores_gemma":[0.00294692,0.0002460409,0.0005940591,0.0003915523,0.001206915,0.0009792285,0.0006717831,0.0008946885,0.0001942572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001291022,"about_ca_system_score_gemma":0.0008190938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002609991,"about_ca_topic_score_gemma":0.001637166,"domain_scores_codex":[0.9994226,0.0001782475,0.0000229966,0.0001434612,0.0001498626,0.00008289264],"domain_scores_gemma":[0.9987082,0.000873739,0.000178385,0.00005781413,0.0001037013,0.00007815346],"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":[0.0001223357,0.00003677702,0.00122619,0.00008703032,0.00003034386,0.0002007154,0.00005396868,0.8853307,0.01011792,0.09069644,0.0004190132,0.01167856],"study_design_scores_gemma":[0.000007417191,0.00001822602,0.000149253,0.000004045576,0.000009169557,0.00002504133,0.000006861724,0.9754604,0.001545057,0.02232689,0.0004420275,0.000005670467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.123842,0.00047542,0.8696133,0.0004022251,0.00007293576,0.00005318425,0.0001973296,0.0003587382,0.004984661],"genre_scores_gemma":[0.9600441,0.000383926,0.03734023,0.000139826,0.00003175817,0.0001293985,0.0001061892,0.00004000876,0.001784566],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002609991,"threshold_uncertainty_score":0.009367049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008619950761029931,"score_gpt":0.2289871581378344,"score_spread":0.2203672073768045,"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."}}