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a genome-wide gene function prediction resource for drosophila melanogaster全基因组基因功能预测资源黑腹果蝇
A Genome-Wide Gene Function Prediction Resource for
Drosophila melanogaster
1,2 1,2¤a 5 1,2¤c 1,2,4¤b
Han Yan , Kavitha Venkatesan , John E. Beaver , Niels Klitgord , Muhammed A. Yildirim ,
Tong Hao1,2, David E. Hill1,2, Michael E. Cusick1,2, Norbert Perrimon2,3, Frederick P. Roth1,5*, Marc
Vidal1,2*
1 Department of Cancer Biology, Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America, 2 Department
of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America, 3 Howard Hughes Medical Institute, Boston, Massachusetts, United States of
America, 4 Applied Physics Program, Division of Engineering and Applied Sciences, Graduate School of Arts and Sciences, Harvard University, Cambridge, Massachusetts,
United States of America, 5 Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts, United States of America
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 th
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