{"id":"W2031700554","doi":"10.1155/2012/245968","title":"An Integrative Approach to Infer Regulation Programs in a Transcription Regulatory Module Network","year":2012,"lang":"en","type":"article","venue":"Journal of Biomedicine and Biotechnology","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Gene regulatory network; Data mining; Bayesian network; Intersection (aeronautics); Cluster analysis; Transcription (linguistics); Set (abstract data type); Rank (graph theory); Computational biology; Machine learning; Theoretical computer science; Gene; Biology; Gene expression; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061158,0.0001186173,0.0002171665,0.0002067059,0.00003068338,0.000009118714,0.0001184596,0.0003653267,0.000001645534],"category_scores_gemma":[0.00001261486,0.00008394533,0.00003892887,0.0002633656,0.0001286503,0.00001863431,0.00002937812,0.00019625,4.872979e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001974979,"about_ca_system_score_gemma":0.00002506509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005905359,"about_ca_topic_score_gemma":0.0000117657,"domain_scores_codex":[0.9991298,0.00003583999,0.0003847372,0.0001107386,0.00009015457,0.0002487495],"domain_scores_gemma":[0.9994959,0.000003029169,0.0001662144,0.0001604581,0.00005397977,0.0001204222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000416157,0.0005145115,0.009392952,0.00004426785,0.0001021273,0.000001846202,0.001316514,0.0002565044,0.7090312,0.002861612,0.002875071,0.2731872],"study_design_scores_gemma":[0.01627303,0.03828963,0.3233842,0.001437273,0.0004978891,0.003790476,0.01744185,0.0184765,0.1948843,0.01207214,0.3705659,0.002886762],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9480035,0.001761013,0.04899446,0.0006937832,0.000206696,0.0001923332,0.000001164541,0.000006956156,0.0001400707],"genre_scores_gemma":[0.9884253,0.0002532339,0.01038587,0.0002677451,0.0005997203,0.000004784221,0.00003105323,0.000008532945,0.00002381992],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5141469,"threshold_uncertainty_score":0.3423191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01136044514881887,"score_gpt":0.2347768566456771,"score_spread":0.2234164114968582,"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."}}