{"id":"W2116042682","doi":"10.5402/2012/419419","title":"Hybrid-Controlled Neurofuzzy Networks Analysis Resulting in Genetic Regulatory Networks Reconstruction","year":2012,"lang":"en","type":"article","venue":"ISRN Bioinformatics","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Gene regulatory network; Computer science; Data mining; Fuzzy logic; Genetic algorithm; Artificial intelligence; Expression (computer science); Machine learning; Algorithm; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001050953,0.0003813865,0.0006897366,0.0004498347,0.0001598766,0.00006916047,0.000313825,0.0003115019,0.00002481177],"category_scores_gemma":[0.0001177812,0.0003721569,0.0005456223,0.0009603159,0.0001158218,0.0000371092,0.0001706463,0.000277883,0.00001414451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007125692,"about_ca_system_score_gemma":0.00005258822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001769045,"about_ca_topic_score_gemma":0.0001177505,"domain_scores_codex":[0.9970279,0.000209368,0.001230987,0.0003367548,0.0002794269,0.0009155813],"domain_scores_gemma":[0.9980824,0.00005120443,0.0005799964,0.0009161646,0.0001154766,0.000254755],"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":[0.0001957896,0.00006527405,0.2301411,0.00001841141,0.001017795,0.000002644177,0.00008205959,0.7445353,0.0004658647,0.00002184596,0.0006442977,0.0228096],"study_design_scores_gemma":[0.001959684,0.00005722239,0.1024284,0.0000193731,0.0008257779,0.00005859258,0.0001411355,0.8928151,0.0005008355,0.00001493898,0.0006892455,0.0004896959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9242999,0.002236217,0.07155507,0.00002777115,0.000504928,0.0003979094,0.000005480194,0.00004359601,0.0009291211],"genre_scores_gemma":[0.9909667,0.0004984269,0.007001039,0.0002459582,0.0008275335,0.00003701238,0.0001900075,0.0000404278,0.0001928327],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1482798,"threshold_uncertainty_score":0.999873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00622289787016415,"score_gpt":0.2142511547049468,"score_spread":0.2080282568347827,"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."}}