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.
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References
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© 2006 Springer-Verlag Berlin Heidelberg
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Nishikawa, I., Sakamoto, H., Nouno, I., Iritani, T., Sakakibara, K., Ito, M. (2006). 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) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893004_122
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DOI: https://doi.org/10.1007/11893004_122
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46537-9
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