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A computational method for identification of vaccine targets from protein regions of conserved human leukocyte antigen binding

Overview of attention for article published in BMC Medical Genomics, December 2015
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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1 Wikipedia page

Citations

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1 Dimensions

Readers on

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33 Mendeley
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Title
A computational method for identification of vaccine targets from protein regions of conserved human leukocyte antigen binding
Published in
BMC Medical Genomics, December 2015
DOI 10.1186/1755-8794-8-s4-s1
Pubmed ID
Authors

Lars R Olsen, Christian Simon, Ulrich J Kudahl, Frederik O Bagger, Ole Winther, Ellis L Reinherz, Guang L Zhang, Vladimir Brusic

Abstract

Computational methods for T cell-based vaccine target discovery focus on selection of highly conserved peptides identified across pathogen variants, followed by prediction of their binding of human leukocyte antigen molecules. However, experimental studies have shown that T cells often target diverse regions in highly variable viral pathogens and this diversity may need to be addressed through redefinition of suitable peptide targets. We have developed a method for antigen assessment and target selection for polyvalent vaccines, with which we identified immune epitopes from variable regions, where all variants bind HLA. These regions, although variable, can thus be considered stable in terms of HLA binding and represent valuable vaccine targets. We applied this method to predict CD8+ T-cell targets in influenza A H7N9 hemagglutinin and significantly increased the number of potential vaccine targets compared to the number of targets discovered using the traditional approach where low-frequency peptides are excluded. We developed a webserver with an intuitive visualization scheme for summarizing the T cell-based antigenic potential of any given protein or proteome using human leukocyte antigen binding predictions and made a web-accessible software implementation freely available at http://met-hilab.cbs.dtu.dk/blockcons/.

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Denmark 1 3%
Unknown 32 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 18%
Student > Ph. D. Student 5 15%
Researcher 5 15%
Student > Bachelor 5 15%
Professor 3 9%
Other 7 21%
Unknown 2 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 30%
Biochemistry, Genetics and Molecular Biology 8 24%
Immunology and Microbiology 4 12%
Engineering 2 6%
Medicine and Dentistry 2 6%
Other 4 12%
Unknown 3 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 June 2017.
All research outputs
#3,158,502
of 11,357,505 outputs
Outputs from BMC Medical Genomics
#187
of 527 outputs
Outputs of similar age
#107,199
of 345,600 outputs
Outputs of similar age from BMC Medical Genomics
#9
of 15 outputs
Altmetric has tracked 11,357,505 research outputs across all sources so far. This one is in the 49th percentile – i.e., 49% of other outputs scored the same or lower than it.
So far Altmetric has tracked 527 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has gotten more attention than average, scoring higher than 59% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 345,600 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.