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Immunoinformatics and the prediction of immunogenicity.

Authors: Darren R, Flower; Irini A, Doytchinova;

Immunoinformatics and the prediction of immunogenicity.

Abstract

Immunoinformatics is the application of informatics techniques to molecules of the immune system. One of the key goals of immunoinformatics is the development of computer aided vaccine design (CAVD), or computational vaccinology, and its application to the search for new vaccines. Key to solving this challenge is the prediction of immunogenicity, be that at the level of epitope, subunit vaccine or attenuated pathogen. This paper reviews the current state of play in the prediction of immunogenicity and focuses on well developed methods for the prediction of peptide binding affinity to major histocompatibility complexes, which are the necessary preliminary to the in silico identification of T cell epitopes.

Related Organizations
Keywords

T-Lymphocytes, Models, Immunological, Computational Biology, Major Histocompatibility Complex, Epitopes, Kinetics, Allergy and Immunology, Animals, Humans, Protein Binding

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    Average
    influence
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
8
Average
Top 10%
Top 10%
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