{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003537846,0.0001376472,0.0001467781,0.00007582,0.0000441481,0.00001192608,0.0001099884,0.0001722486,0.00002176009],"category_scores_gemma":[0.00002073079,0.0001366042,0.0001044666,0.0002439259,0.00003503758,0.00000303534,0.00002734767,0.00007697331,0.000006956393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000285272,"about_ca_system_score_gemma":0.0000180078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001264545,"about_ca_topic_score_gemma":0.00015025,"domain_scores_codex":[0.9989291,0.0001565167,0.0002257791,0.0003392074,0.0001057161,0.0002436868],"domain_scores_gemma":[0.9994694,0.000007422305,0.00006082567,0.0003459735,0.00004912932,0.00006726903],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009370922,0.00009952191,0.0006497211,0.000002250511,0.00007307449,0.000004794057,0.0002767911,0.04670031,0.8979457,0.000174519,0.0009136411,0.05306595],"study_design_scores_gemma":[0.0007976549,0.0004023662,0.06638936,0.000009594208,0.00003956405,0.00001439407,0.00058536,0.02000597,0.9092271,0.0008440804,0.001179202,0.0005053063],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9579382,0.0008277288,0.03994536,0.00007974114,0.00006143193,0.00009595752,5.568601e-7,0.00001864603,0.001032369],"genre_scores_gemma":[0.997606,0.00007333429,0.0008635823,0.0002793008,0.0002285365,0.000006405557,0.00003469436,0.00001192016,0.0008962406],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06573964,"threshold_uncertainty_score":0.5570559,"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."}}