{"id":"W2165850410","doi":"10.1186/1756-0500-4-462","title":"Functional Genomics Assistant (FUGA): a toolbox for the analysis of complex biological networks","year":2011,"lang":"en","type":"article","venue":"BMC Research Notes","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Fondation Leducq; European Commission; King's College London; British Heart Foundation","keywords":"Biological network; Computer science; Toolbox; Systems biology; Functional genomics; Computational biology; Gene regulatory network; Context (archaeology); Inference; Genomics; Artificial intelligence; Biology; Genome; Gene; Genetics; Gene expression; Programming language","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":[0.001419999,0.0001066504,0.0001955199,0.00009371081,0.0002157914,0.00002761714,0.0003467655,0.0001624364,0.000123444],"category_scores_gemma":[0.0002586166,0.00006800786,0.000265132,0.000337542,0.0003558119,0.00000261291,0.0001774699,0.0001397477,0.000003165473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001655536,"about_ca_system_score_gemma":0.00009989137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006051595,"about_ca_topic_score_gemma":0.0002333823,"domain_scores_codex":[0.9988476,0.00009905588,0.0003034923,0.000222842,0.0001635432,0.0003634503],"domain_scores_gemma":[0.9986696,0.0004741388,0.00009243643,0.0004221211,0.0002716278,0.00007011892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.01427218,0.00181163,0.2175222,0.0003136634,0.01914568,0.000005568991,0.001833531,0.1168984,0.3500043,0.05606408,0.09047027,0.1316585],"study_design_scores_gemma":[0.001213443,0.00110628,0.5671641,0.00001160715,0.0004422006,0.000005217217,0.0007018507,0.3655512,0.008703333,0.001917294,0.05271437,0.000469122],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.261487,0.001623145,0.7346352,0.000121858,0.0001541478,0.0007045451,0.0001620391,0.000009512345,0.001102514],"genre_scores_gemma":[0.9924137,0.0002330582,0.006493249,0.00008309961,0.0002527144,0.00006975029,0.0003302501,0.00001004344,0.0001141371],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7309267,"threshold_uncertainty_score":0.277328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.324900467290071,"score_gpt":0.3675908522975609,"score_spread":0.04269038500748989,"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."}}