5.00
Hdl Handle:
http://hdl.handle.net/10033/621179
Title:
In Silico Vaccine Strain Prediction for Human Influenza Viruses.
Authors:
Klingen, Thorsten R; Reimering, Susanne; Guzmán, Carlos A; McHardy, Alice C
Abstract:
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.
Affiliation:
Braunschweiger Zentrum für Systembiology, Rebenring 56,38108 Braunschweig, Germany.
Citation:
In Silico Vaccine Strain Prediction for Human Influenza Viruses. 2017 Trends Microbiol.
Journal:
Trends in microbiology
Issue Date:
9-Oct-2017
URI:
http://hdl.handle.net/10033/621179
DOI:
10.1016/j.tim.2017.09.001
PubMed ID:
29032900
Type:
Article
Language:
en
ISSN:
1878-4380
Appears in Collections:
publications of the research group bioinformatics in infection research ([BRICS] BIFO)

Full metadata record

DC FieldValue Language
dc.contributor.authorKlingen, Thorsten Ren
dc.contributor.authorReimering, Susanneen
dc.contributor.authorGuzmán, Carlos Aen
dc.contributor.authorMcHardy, Alice Cen
dc.date.accessioned2017-11-20T11:53:09Z-
dc.date.available2017-11-20T11:53:09Z-
dc.date.issued2017-10-09-
dc.identifier.citationIn Silico Vaccine Strain Prediction for Human Influenza Viruses. 2017 Trends Microbiol.en
dc.identifier.issn1878-4380-
dc.identifier.pmid29032900-
dc.identifier.doi10.1016/j.tim.2017.09.001-
dc.identifier.urihttp://hdl.handle.net/10033/621179-
dc.description.abstractVaccines 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.en
dc.language.isoenen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.titleIn Silico Vaccine Strain Prediction for Human Influenza Viruses.en
dc.typeArticleen
dc.contributor.departmentBraunschweiger Zentrum für Systembiology, Rebenring 56,38108 Braunschweig, Germany.en
dc.identifier.journalTrends in microbiologyen

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