{"id":"W2147784911","doi":"10.1109/bibm.2010.5706595","title":"A dynamic qualitative probabilistic network approach for extracting gene regulatory network motifs","year":2010,"lang":"en","type":"article","venue":"","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Gene regulatory network; Computer science; Motif (music); Saccharomyces cerevisiae; Probabilistic logic; Computational biology; Network motif; Gene; Network analysis; Theoretical computer science; Biological network; Data mining; Genetics; Artificial intelligence; Biology; Gene expression; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001531454,0.0003522612,0.000376854,0.00003981211,0.0003240129,0.00005135223,0.000354713,0.0003824778,0.00003683786],"category_scores_gemma":[0.0002014277,0.0003352627,0.0002962326,0.0002863057,0.0001821591,0.000007010093,0.0001455378,0.00024781,0.000005373204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002832804,"about_ca_system_score_gemma":0.0001173079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006729632,"about_ca_topic_score_gemma":0.0001392653,"domain_scores_codex":[0.9974617,0.0001936398,0.0004924295,0.0008626689,0.0002112551,0.0007782911],"domain_scores_gemma":[0.9983006,0.0001088807,0.000264601,0.0009044405,0.0002285978,0.0001929194],"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":[0.0002563637,0.0002520822,0.001000936,0.0001016044,0.0008237304,0.000001997763,0.0004093541,0.488261,0.4834883,0.003799666,0.01524351,0.006361426],"study_design_scores_gemma":[0.003186264,0.001033602,0.009502986,0.0000560528,0.001117644,0.0001470625,0.001840331,0.9115903,0.02645433,0.01687688,0.0246942,0.003500293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6686682,0.0008387844,0.327296,0.00006998825,0.0003847734,0.0009197526,0.00001343911,0.00007218165,0.001736887],"genre_scores_gemma":[0.775825,0.00001282137,0.219876,0.0001216431,0.00126131,0.0001983195,0.0004398817,0.00007057952,0.002194433],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.457034,"threshold_uncertainty_score":0.9999099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01298201831527137,"score_gpt":0.2817624900784552,"score_spread":0.2687804717631839,"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."}}