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Immunoinformatics in personalized medicine.

Authors: Kamalakar, Gulukota;

Immunoinformatics in personalized medicine.

Abstract

Diagnosis of human disease has been undergoing steady improvement over the past few centuries. Many ailments that were once considered a single entity have been classified into finer categories on the basis of response to therapy (e.g. type I and type II diabetes), inheritance (e.g. familial and non-familial polyposis coli), histology (e.g. small cell and adenocarcinoma of lung) and most recently transcriptional profiling (e.g. leukaemia, lymphoma). The next dimension in this finer categorization appears to be the typing of the patient rather than the disease i.e. disease X in person of type Y. The problem of personalized medicine is to devise tests which predict the type of individual, especially where the type is correlated with response to therapy. Immunology has been at the forefront of personalized medicine for quite a while, even though the term is not often used in this connection. Blood grouping and cross-matching (for blood transfusion), and anaphylaxis test (for penicillin) are just two examples. In this paper I will argue that immunological tests have an important place in the future of personalized medicine. I will describe methods we developed for personalizing vaccines based on MHC allele frequencies in human populations and methods for predicting peptide binding to class I MHC molecules. In conclusion, I will argue that immunological tests, and consequently immunoinformatics, will play a big role in making personalized medicine a reality.

Keywords

HLA Antigens, Pharmacogenetics, Allergy and Immunology, Vaccines, Subunit, Computational Biology, Humans, Alleles

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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!
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