Abstract
Peptides binding to MHC molecules can be presented to T-cell receptor and then trigger an immune response. Prediction of peptides binding a specific major histocompatibility complex has great significance for immunology research and vaccine design. According to their different structures and functions, MHC molecules can be classified into two types. Most of early studies often focus on MHC class I, but seldom on MHC class II. In this paper, we present a method for MHC class II binding peptides prediction using Fourier analysis and support vector machines (SVM), the novel prediction technique is found to be comparable with the best software currently available.
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© 2005 Springer-Verlag Berlin Heidelberg
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Huang, J., Shi, F. (2005). Prediction of MHC class II Epitopes Using Fourier Analysis and Support Vector Machines. In: Abraham, A., Dote, Y., Furuhashi, T., Köppen, M., Ohuchi, A., Ohsawa, Y. (eds) Soft Computing as Transdisciplinary Science and Technology. Advances in Soft Computing, vol 29. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32391-0_10
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DOI: https://doi.org/10.1007/3-540-32391-0_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25055-5
Online ISBN: 978-3-540-32391-4
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