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Prediction of the O-glycosylation Sites in Protein by Layered Neural Networks and Support Vector Machines

  • Ikuko Nishikawa
  • Hirotaka Sakamoto
  • Ikue Nouno
  • Takeshi Iritani
  • Kazutoshi Sakakibara
  • Masahiro Ito
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4252)

Abstract

O-glycosylation is one of the main types of the mammalian protein glycosylation, which is serine or threonine specific, though any consensus sequence is still unknown. In this paper, a layered neural network and a support vector machine are used for the prediction of O-glycosylation sites. Three types of encoding for a protein sequence within a fixed size window are used as the input to the network, that is, a sparse coding which distinguishes all 20 amino acid residues, 5-letter coding and hydropathy coding. In the neural network, one output unit gives the prediction whether a particular site of serine or threonine is glycosylated, while SVM classifies into the 2 classes. The performance is evaluated by the Matthews correlation coefficient. The preliminary results on the neural network show the better performance of the sparse and 5-letter codings compared with the hydropathy coding, while the improvement according to the window size is shown to be limited to a certain extent by SVM.

Keywords

Support Vector Machine Window Size Feedforward Neural Network Sparse Code Matthews Correlation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    Julenius, K., Molgaard, A., Gupta, R., Brunak, S.: Prediction, conservation analysis and structural characterization of mammalian mucin-type O-glycosylation sites. Glycobiology 15(2), 153–164 (2004)CrossRefGoogle Scholar
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    Julenius, K., Molgaard, A., Gupta, R., Brunak, S.: Supplementary material on Prediction, conservation analysis and structural characterization of mammalian mucin-type O-glycosylation sites (2004)Google Scholar
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    Cristianini, N., Taylor, J.S.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge Univ. Press, Cambridge (2000)Google Scholar
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ikuko Nishikawa
    • 1
  • Hirotaka Sakamoto
    • 1
  • Ikue Nouno
    • 1
  • Takeshi Iritani
    • 1
  • Kazutoshi Sakakibara
    • 1
  • Masahiro Ito
    • 1
  1. 1.College of Information Science and EngineeringRitsumeikan UniversityKusatsuJapan

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