Summary
Prediction of class II major histocompatibility complex (MHC)–peptide binding is a challenging task due to variable length of binding peptides. Different computational methods have been developed; however, each has its own strength and weakness. In order to provide reliable prediction, it is important to design a system that enables the integration of outcomes from various predictors. In this chapter, the procedure of building such a meta-predictor based on Naïve Bayesian approach is introduced. The system is designed in such a way that results obtained from any number of individual predictors can be easily incorporated. This meta-predictor is expected to give users more confidence in the prediction.
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Acknowledgments
This work is partially supported by the NIH grant (1 R03 AI069391-01).
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Huang, L., Karpenko, O., Murugan, N., Dai, Y. (2007). Building a Meta-Predictor for MHC Class II-Binding Peptides. In: Flower, D.R. (eds) Immunoinformatics. Methods in Molecular Biology™, vol 409. Humana Press. https://doi.org/10.1007/978-1-60327-118-9_26
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DOI: https://doi.org/10.1007/978-1-60327-118-9_26
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Online ISBN: 978-1-60327-118-9
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