{"id":"W2159389525","doi":"10.1109/bibm.2009.80","title":"Qualitative Motif Detection in Gene Regulatory Networks","year":2009,"lang":"en","type":"article","venue":"","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Dynamic Bayesian network; ENCODE; Bayesian network; Computer science; Gene regulatory network; Probabilistic logic; Bayesian probability; Artificial intelligence; Machine learning; Data mining; Computational biology; Gene; Gene expression; Biology; Genetics","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":[0.001770022,0.0004030721,0.0004948206,0.001364118,0.0003060388,0.000785288,0.0009665401,0.0004698462,0.0007839937],"category_scores_gemma":[0.0106398,0.0004653809,0.0006489785,0.000983828,0.001381993,0.001391622,0.0007533503,0.0007102292,0.0001120051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001227204,"about_ca_system_score_gemma":0.0005835351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003554157,"about_ca_topic_score_gemma":0.002257843,"domain_scores_codex":[0.9989557,0.0004357175,0.00004397603,0.0002458698,0.0002642138,0.00005446835],"domain_scores_gemma":[0.9949275,0.003868982,0.0005332849,0.0002759186,0.0002986272,0.00009563762],"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":[0.0001831803,0.00004341119,0.007227296,0.0002652672,0.00007640636,0.0002235637,0.0003789742,0.7582783,0.0267158,0.1377539,0.0002887632,0.06856509],"study_design_scores_gemma":[0.000005972267,0.00001618936,0.000841076,0.00001196867,0.00001009196,0.00006258622,0.00002610261,0.901529,0.003555669,0.09338112,0.0005474187,0.00001290451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03847677,0.0001132383,0.9606337,0.00005845855,0.00000409793,0.00001479403,0.00009088887,0.0002331697,0.000374805],"genre_scores_gemma":[0.7646213,0.0002683099,0.2341075,0.0000523601,0.00001192369,0.00008389571,0.0002712013,0.00006522773,0.000518214],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003554157,"threshold_uncertainty_score":0.00936085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009974400700667648,"score_gpt":0.2754732092857959,"score_spread":0.2654988085851283,"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."}}