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From Functional Genomics to Functional Immunomics: New Challenges, Old Problems, Big Rewards

Overview of attention for article published in PLoS Computational Biology, July 2006
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Title
From Functional Genomics to Functional Immunomics: New Challenges, Old Problems, Big Rewards
Published in
PLoS Computational Biology, July 2006
DOI 10.1371/journal.pcbi.0020081
Pubmed ID
Authors

Ulisses M Braga-Neto, Ernesto T A Marques

Abstract

The development of DNA microarray technology a decade ago led to the establishment of functional genomics as one of the most active and successful scientific disciplines today. With the ongoing development of immunomic microarray technology-a spatially addressable, large-scale technology for measurement of specific immunological response-the new challenge of functional immunomics is emerging, which bears similarities to but is also significantly different from functional genomics. Immunonic data has been successfully used to identify biological markers involved in autoimmune diseases, allergies, viral infections such as human immunodeficiency virus (HIV), influenza, diabetes, and responses to cancer vaccines. This review intends to provide a coherent vision of this nascent scientific field, and speculate on future research directions. We discuss at some length issues such as epitope prediction, immunomic microarray technology and its applications, and computation and statistical challenges related to functional immunomics. Based on the recent discovery of regulation mechanisms in T cell responses, we envision the use of immunomic microarrays as a tool for advances in systems biology of cellular immune responses, by means of immunomic regulatory network models.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 115 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 3%
United Kingdom 4 3%
Germany 3 3%
South Africa 3 3%
Chile 1 <1%
Hungary 1 <1%
India 1 <1%
Colombia 1 <1%
Brazil 1 <1%
Other 4 3%
Unknown 92 80%

Demographic breakdown

Readers by professional status Count As %
Researcher 31 27%
Student > Ph. D. Student 27 23%
Professor > Associate Professor 10 9%
Professor 10 9%
Other 8 7%
Other 23 20%
Unknown 6 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 56 49%
Medicine and Dentistry 16 14%
Immunology and Microbiology 10 9%
Biochemistry, Genetics and Molecular Biology 8 7%
Computer Science 3 3%
Other 11 10%
Unknown 11 10%