Abstract
Processing and presentation of major histocompatibility complex class I antigens to cytotoxic T-lymphocytes is crucial for immune surveillance against intracellular bacteria, parasites, viruses and tumors. Identification of antigenic regions on pathogen proteins will play a pivotal role in designer vaccine immunotherapy. We have developed a novel method that identifies MHC class I binding peptides from peptides sequences. For the first time we present a method for MHC class I binding peptides prediction using Fourier analysis and support vector machines (SVM). Using cross-validation, we demonstrate that this novel prediction technique has a reasonable performance.
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Shi, F., Chen, Q. (2006). Prediction of MHC Class I Binding Peptides Using Fourier Analysis and Support Vector Machine. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2006. Lecture Notes in Computer Science(), vol 4223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881599_133
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DOI: https://doi.org/10.1007/11881599_133
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45916-3
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