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Machine Learning-Based Methods for Prediction of Linear B-Cell Epitopes

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Immunoinformatics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1184))

Abstract

B-cell epitope prediction facilitates immunologists in designing peptide-based vaccine, diagnostic test, disease prevention, treatment, and antibody production. In comparison with T-cell epitope prediction, the performance of variable length B-cell epitope prediction is still yet to be satisfied. Fortunately, due to increasingly available verified epitope databases, bioinformaticians could adopt machine learning-based algorithms on all curated data to design an improved prediction tool for biomedical researchers. Here, we have reviewed related epitope prediction papers, especially those for linear B-cell epitope prediction. It should be noticed that a combination of selected propensity scales and statistics of epitope residues with machine learning-based tools formulated a general way for constructing linear B-cell epitope prediction systems. It is also observed from most of the comparison results that the kernel method of support vector machine (SVM) classifier outperformed other machine learning-based approaches. Hence, in this chapter, except reviewing recently published papers, we have introduced the fundamentals of B-cell epitope and SVM techniques. In addition, an example of linear B-cell prediction system based on physicochemical features and amino acid combinations is illustrated in details.

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Acknowledgements

This work is supported by the Center of Excellence for the Oceans, National Taiwan Ocean University, and National Science Council, Taiwan, R.O.C. (NSC 102-2321-B-019-001 and NSC 102-2221-E-019-059 to T.-W. Pai).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Taiwan Ocean University, 2 Pei-Ning Road, Keelung, 20224, Taiwan, Republic of China

    Hsin-Wei Wang & Tun-Wen Pai

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  1. Hsin-Wei Wang
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  2. Tun-Wen Pai
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Correspondence to Tun-Wen Pai .

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Editors and Affiliations

  1. Indian Statistical Institute, Kolkata, West Bengal, India

    Rajat K. De

  2. Indian Statistical Institute, Kolkata, West Bengal, India

    Namrata Tomar

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Wang, HW., Pai, TW. (2014). Machine Learning-Based Methods for Prediction of Linear B-Cell Epitopes. In: De, R., Tomar, N. (eds) Immunoinformatics. Methods in Molecular Biology, vol 1184. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-1115-8_12

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  • DOI: https://doi.org/10.1007/978-1-4939-1115-8_12

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  • Online ISBN: 978-1-4939-1115-8

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