{"id":"W2125939853","doi":"10.1371/journal.pone.0012139","title":"A Genome-Wide Gene Function Prediction Resource for Drosophila melanogaster","year":2010,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Human Genome Research Institute; National Institutes of Health; Howard Hughes Medical Institute; Fédération Wallonie-Bruxelles; Canadian Institute for Advanced Research; Harvard University; National Institute of Diabetes and Digestive and Kidney Diseases; Fonds De La Recherche Scientifique - FNRS","keywords":"Drosophila melanogaster; Computational biology; Biology; KEGG; Function (biology); Drosophila (subgenus); Genome; Gene; Melanogaster; RNA interference; Model organism; Resource (disambiguation); Genetics; Computer science; Gene ontology; Gene expression; RNA","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001646435,0.0001103046,0.0001100501,0.00002869899,0.00009662313,0.00002746313,0.0001084855,0.0002011193,0.00002680325],"category_scores_gemma":[0.0000344564,0.0001088328,0.00006337227,0.00003497841,0.00003447059,0.000004165786,0.00005637667,0.0001249711,0.00001890232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006405764,"about_ca_system_score_gemma":0.00002104651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.970712e-7,"about_ca_topic_score_gemma":0.00000957934,"domain_scores_codex":[0.9992965,0.00000916942,0.0001914526,0.0002001382,0.0001010694,0.0002017028],"domain_scores_gemma":[0.9994563,0.00001044915,0.00007578662,0.000310791,0.00007167262,0.00007500526],"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":[0.0001571761,0.0001558166,0.00141336,0.00003360095,0.0001536933,1.5328e-7,0.00003292687,0.00002353524,0.9963393,0.00004516131,0.001125256,0.0005200732],"study_design_scores_gemma":[0.003936371,0.002605815,0.03279576,0.00005790028,0.0006190513,0.00001942529,0.00008508182,0.007311291,0.6018208,0.002698523,0.3471645,0.0008855542],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9872085,0.0001267388,0.009823228,0.0002508313,0.0001410127,0.0004136466,0.00007602129,0.0000233309,0.001936683],"genre_scores_gemma":[0.9889455,0.00003646145,0.005992195,0.000705805,0.001498011,0.00009172309,0.0009067226,0.00003036998,0.001793144],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3945185,"threshold_uncertainty_score":0.4438073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01344151819634904,"score_gpt":0.18680142345353,"score_spread":0.173359905257181,"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."}}