{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009937363,0.0006243982,0.0005332212,0.0006781676,0.0002786108,0.0005019779,0.0006696957,0.0006610908,0.0007071414],"category_scores_gemma":[0.002213963,0.0003224282,0.0007696845,0.0003708866,0.0004097992,0.000599391,0.0004579153,0.0006327737,0.0001271953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005047272,"about_ca_system_score_gemma":0.0005487953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003609846,"about_ca_topic_score_gemma":0.003005867,"domain_scores_codex":[0.9997271,0.00009088617,0.00001297933,0.00008771433,0.00006228901,0.00001903516],"domain_scores_gemma":[0.9993818,0.0003952793,0.00006322273,0.00003630126,0.0001090523,0.0000143772],"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":[0.00008415228,0.00003427648,0.0009221652,0.00003816696,0.00005735705,0.00007100003,0.00008132497,0.8975179,0.008963339,0.00529321,0.0002849179,0.0866522],"study_design_scores_gemma":[0.000001716334,0.000005260723,0.0000572409,0.000001331476,0.000002319123,0.000007378935,0.000002590549,0.9981231,0.0007726321,0.0009572449,0.00006694603,0.000002180797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01999456,0.0000555475,0.9793706,0.0000392402,0.000006328536,0.00001588653,0.0000244622,0.0001976925,0.0002956768],"genre_scores_gemma":[0.4599347,0.00008451472,0.5383326,0.00005288342,0.00001072264,0.0001242353,0.000146183,0.00004972241,0.00126449],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003609846,"threshold_uncertainty_score":0.007177651,"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."}}