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PREDIVAC: CD4+ T-cell epitope prediction for vaccine design that covers 95% of HLA class II DR protein diversity

Overview of attention for article published in BMC Bioinformatics, February 2013
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1 tweeter

Citations

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105 Mendeley
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Title
PREDIVAC: CD4+ T-cell epitope prediction for vaccine design that covers 95% of HLA class II DR protein diversity
Published in
BMC Bioinformatics, February 2013
DOI 10.1186/1471-2105-14-52
Pubmed ID
Authors

Patricio Oyarzún, Jonathan J Ellis, Mikael Bodén, Boštjan Kobe

Abstract

CD4+ T-cell epitopes play a crucial role in eliciting vigorous protective immune responses during peptide (epitope)-based vaccination. The prediction of these epitopes focuses on the peptide binding process by MHC class II proteins. The ability to account for MHC class II polymorphism is critical for epitope-based vaccine design tools, as different allelic variants can have different peptide repertoires. In addition, the specificity of CD4+ T-cells is often directed to a very limited set of immunodominant peptides in pathogen proteins. The ability to predict what epitopes are most likely to dominate an immune response remains a challenge.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 105 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Argentina 2 2%
India 1 <1%
Kenya 1 <1%
Saudi Arabia 1 <1%
United States 1 <1%
Unknown 99 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 22%
Student > Master 22 21%
Researcher 17 16%
Student > Bachelor 9 9%
Professor > Associate Professor 7 7%
Other 18 17%
Unknown 9 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 32 30%
Biochemistry, Genetics and Molecular Biology 22 21%
Immunology and Microbiology 10 10%
Computer Science 7 7%
Engineering 6 6%
Other 15 14%
Unknown 13 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 14 February 2013.
All research outputs
#18,986,808
of 21,344,814 outputs
Outputs from BMC Bioinformatics
#6,511
of 6,922 outputs
Outputs of similar age
#248,075
of 282,224 outputs
Outputs of similar age from BMC Bioinformatics
#32
of 32 outputs
Altmetric has tracked 21,344,814 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,922 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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 282,224 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 32 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.