Summary
Identifying epitopes that elicit a major histocompatibility complex (MHC)-restricted T-cell response is critical for designing vaccines for infectious diseases and cancers. We have applied two artificial intelligence approaches to build models for predicting T-cell epitopes. We developed a support vector machine to predict T-cell epitopes for an MHC class I-restricted T-cell clone (TCC) using synthesized peptide data. For predicting T-cell epitopes for an MHC class IIrestricted TCC, we built a shift model that integrated MHC-binding data and data from T-cell proliferation assay against a combinatorial library of peptide mixtures
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Zhao, Y., Sung, MH., Simon, R. (2007). Artificial Intelligence Methods for Predicting T-Cell Epitopes. In: Flower, D.R. (eds) Immunoinformatics. Methods in Molecular Biology™, vol 409. Humana Press. https://doi.org/10.1007/978-1-60327-118-9_15
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DOI: https://doi.org/10.1007/978-1-60327-118-9_15
Publisher Name: Humana Press
Print ISBN: 978-1-58829-699-3
Online ISBN: 978-1-60327-118-9
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