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X Demographics
Mendeley readers
Attention Score in Context
| 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. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Norway | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 111 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 | 105 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 26 | 23% |
| Student > Master | 23 | 21% |
| Researcher | 18 | 16% |
| Student > Bachelor | 9 | 8% |
| Professor > Associate Professor | 7 | 6% |
| Other | 17 | 15% |
| Unknown | 11 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 32 | 29% |
| Biochemistry, Genetics and Molecular Biology | 21 | 19% |
| Immunology and Microbiology | 14 | 13% |
| Computer Science | 8 | 7% |
| Engineering | 6 | 5% |
| Other | 14 | 13% |
| Unknown | 16 | 14% |
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
#20,182,546
of 22,696,971 outputs
Outputs from BMC Bioinformatics
#6,827
of 7,254 outputs
Outputs of similar age
#253,533
of 287,569 outputs
Outputs of similar age from BMC Bioinformatics
#134
of 141 outputs
Altmetric has tracked 22,696,971 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 7,254 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 287,569 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 141 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.