Abstract
Glycosylation is one of the main topics in understanding the life systems. More than a half of the protein is glycosylated to acquire the function, structural stability and biological diversity. O-glycosylation is one of the two main types of the mammalian protein glycosylation. Though it is known to serine or threonine specific, any consensus sequence is still unknown, while the binding process and the consensus sequence are clarified for the other type of N-glycosylation. We use support vector machines (SVM) for the prediction of O-glycosylation sites using the experimental data as the input information such as protein primary sequences, structural and biochemical characters around a prediction target aiming to elucidate the glycosylation mechanism and the existence of any motives. The present paper also reports the results obtained by the semi-supervised learning using transductive SVM considering a possibility of unobserved glycosylation sites, and by the marginalized kernel considering hidden variables.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Taylor, M.E., Drickamer, K.: Introduction to Glycobiology. Oxford University Press, Oxford (2003)
Cristianini, N., Taylor, J.S.: An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press, Cambridge (2000)
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)
Li, S., et al.: Predicting O-glycosylation sites in mammalian proteins by using SVMs. Computational Biology and Chemistry 30, 203–208 (2006)
Nishikawa, I., et al.: Prediction of the O-glycosylation sites in protein by layered neural networks and support vector machines. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds.) KES 2006. LNCS (LNAI), vol. 4252, pp. 953–960. Springer, Heidelberg (2006)
Nishikawa, I., et al.: Prediction of the O-Glycosylation with Secondary Structure Information by Support Vector Machines. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) KES 2007, Part II. LNCS (LNAI), vol. 4693, pp. 335–343. Springer, Heidelberg (2007)
Sakamoto, H., et al.: Prediction of Mucin-type O-glycosylation using Structure Information by Support Vector Machines. In: Proceedings of the 18th Int. Conference on Genome Informatics (December 2007)
Zhu, X.: Semi-Supervised Learning Literature Survay, Computer Sciences TR 1530, University of Wisconsisn-Madison (2007)
Tsuda, K., et al.: Marginalized kernels for biological sequences. Bioinformatics 18(suppl. 1), 268–275 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sakamoto, H., Nakajima, Y., Sakakibara, K., Ito, M., Nishikawa, I. (2009). Prediction of the O-Glycosylation by Support Vector Machines and Semi-supervised Learning. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02490-0_120
Download citation
DOI: https://doi.org/10.1007/978-3-642-02490-0_120
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02489-4
Online ISBN: 978-3-642-02490-0
eBook Packages: Computer ScienceComputer Science (R0)