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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...

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Detalles Bibliográficos
Autores principales: Yan, Han, Venkatesan, Kavitha, Beaver, John E., Klitgord, Niels, Yildirim, Muhammed A., Hao, Tong, Hill, David E., Cusick, Michael E., Perrimon, Norbert, Roth, Frederick P., Vidal, Marc
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2920829/
https://www.ncbi.nlm.nih.gov/pubmed/20711346
http://dx.doi.org/10.1371/journal.pone.0012139
Descripción
Sumario: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.