A genome-wide gene function prediction resource for Drosophila melanogaster.
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.
Yan
Han
H
Department of Cancer Biology, Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America.
Venkatesan
Kavitha
K
Beaver
John E
JE
Klitgord
Niels
N
Yildirim
Muhammed A
MA
Hao
Tong
T
Hill
David E
DE
Cusick
Michael E
ME
Perrimon
Norbert
N
Roth
Frederick P
FP
Vidal
Marc
M
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