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PEDS Advance Access originally published online on July 24, 2007
Protein Engineering Design and Selection 2007 20(8):405-412; doi:10.1093/protein/gzm035
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org

GANNPhos: a new phosphorylation site predictor based on a genetic algorithm integrated neural network

Yu-Rong Tang1, Yong-Zi Chen1, Carlos A. Canchaya2 and Ziding Zhang1,3

1Bioinformatics Center, College of Biological Sciences, China Agricultural University, Beijing 100094, China 2Department of Microbiology, National University of Ireland, Cork Western Road, Cork, Ireland

3 To whom correspondence should be addressed. E-mail: zidingzhang{at}cau.edu.cn

With the advance of modern molecular biology it has become increasingly clear that few cellular processes are unaffected by protein phosphorylation. Therefore, computational identification of phosphorylation sites is very helpful to accelerate the functional understanding of huge available protein sequences obtained from genomic and proteomic studies. Using a genetic algorithm integrated neural network (GANN), a new bioinformatics method named GANNPhos has been developed to predict phosphorylation sites in proteins. Aided by a genetic algorithm to optimize the weight values within the network, GANNPhos has demonstrated a high accuracy of 81.1, 76.7 and 73.3% in predicting phosphorylated S, T and Y sites, respectively. When benchmarked against Back-Propagation neural network and Support Vector Machine algorithms, GANNPhos gives better performance, suggesting the GANN program can be used for other prediction tasks in the field of protein bioinformatics.

Keywords: genetic algorithm/neural network/phosphorylation site/prediction/protein bioinformatics

revised April 3, 2007; accepted June 21, 2007.


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