title: A genome-wide gene function prediction resource for Drosophila melanogaster.
abstract: Predicting gene functions by integrating large-scale biological data remains a challenge for systems biology. Here we present a resource for Drosophila melanogaster gene function predictions. We trained function-specific classifiers to optimize the influence of different biological datasets for each functional category. Our model predicted GO terms and KEGG pathway memberships for Drosophila melanogaster genes with high accuracy, as affirmed by cross-validation, supporting literature evidence, and large-scale RNAi screens. The resulting resource of prioritized associations between Drosophila genes and their potential functions offers a guide for experimental investigations.
authors: Department of Cancer Biology, Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America.
authors: Han
authors: H
authors: Yan
authors: Kavitha
authors: K
authors: Venkatesan
authors: John E
authors: JE
authors: Beaver
authors: Niels
authors: N
authors: Klitgord
authors: Muhammed A
authors: MA
authors: Yildirim
authors: Tong
authors: T
authors: Hao
authors: David E
authors: DE
authors: Hill
authors: Michael E
authors: ME
authors: Cusick
authors: Norbert
authors: N
authors: Perrimon
authors: Frederick P
authors: FP
authors: Roth
authors: Marc
authors: M
authors: Vidal
mesh_terms: D000818:Animals; D030541:Databases, Genetic; D004331:Drosophila melanogaster / Q000235:genetics*; D017344:Genes, Insect / Q000235:genetics*; D023281:Genomics / Q000379:methods*; D034622:RNA Interference; D015203:Reproducibility of Results