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Issue Date
2017-10-09
Metadata
Show full item recordAbstract
Vaccines preventing seasonal influenza infections save many lives every year; however, due to rapid viral evolution, they have to be updated frequently to remain effective. To identify appropriate vaccine strains, the World Health Organization (WHO) operates a global program that continually generates and interprets surveillance data. Over the past decade, sophisticated computational techniques, drawing from multiple theoretical disciplines, have been developed that predict viral lineages rising to predominance, assess their suitability as vaccine strains, link genetic to antigenic alterations, as well as integrate and visualize genetic, epidemiological, structural, and antigenic data. These could form the basis of an objective and reproducible vaccine strain-selection procedure utilizing the complex, large-scale data types from surveillance. To this end, computational techniques should already be incorporated into the vaccine-selection process in an independent, parallel track, and their performance continuously evaluated.Citation
In Silico Vaccine Strain Prediction for Human Influenza Viruses. 2017 Trends Microbiol.Affiliation
Braunschweiger Zentrum für Systembiology, Rebenring 56,38108 Braunschweig, Germany.Journal
Trends in microbiologyPubMed ID
29032900Type
ArticleLanguage
enISSN
1878-4380ae974a485f413a2113503eed53cd6c53
10.1016/j.tim.2017.09.001
Scopus Count
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- Creative Commons
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by-nc-sa/4.0/