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