{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007146863,0.002017214,0.0009668648,0.002444705,0.0006078989,0.000532975,0.001369109,0.0008238326,0.01282119],"category_scores_gemma":[0.002145012,0.000721796,0.0008949388,0.002644492,0.0001570165,0.0007722997,0.0009068768,0.00111172,0.01041254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005520762,"about_ca_system_score_gemma":0.00116606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003985685,"about_ca_topic_score_gemma":0.007131267,"domain_scores_codex":[0.9997413,0.00004557382,0.00002873395,0.00008511047,0.00008325616,0.00001599055],"domain_scores_gemma":[0.9992189,0.0003349162,0.00008693327,0.0001443216,0.0001481751,0.00006661807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001530832,0.0003414024,0.01624978,0.004610718,0.0006662051,0.001559678,0.0003060554,0.01988783,0.1503662,0.006377104,0.6018933,0.1962109],"study_design_scores_gemma":[0.001407135,0.0008033272,0.08993389,0.0007475825,0.0008614836,0.003898662,0.0002925853,0.1800542,0.105466,0.02098671,0.5950766,0.0004718227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.02047135,0.001569516,0.1109074,0.0002589627,0.0001089006,0.000250255,0.7969888,0.0649405,0.004504401],"genre_scores_gemma":[0.03185596,0.0007313185,0.1038273,0.0001274113,0.00002164747,0.0005930836,0.8585652,0.002467638,0.001810538],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01282119,"threshold_uncertainty_score":0.0428912,"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."}}