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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Designing string-of-beads vaccines with optimal spacers
|
|---|---|
| Published in |
Genome Medicine, January 2016
|
| DOI | 10.1186/s13073-016-0263-6 |
| Pubmed ID | |
| Authors |
Benjamin Schubert, Oliver Kohlbacher |
| Abstract |
String-of-beads polypeptides allow convenient delivery of epitope-based vaccines. The success of a polypeptide relies on efficient processing: constituent epitopes need to be recovered while avoiding neo-epitopes from epitope junctions. Spacers between epitopes are employed to ensure this, but spacer selection is non-trivial.We present a framework to determine optimally the length and sequence of a spacer through multi-objective optimization for human leukocyte antigen class I restricted polypeptides. The method yields string-of-bead vaccines with flexible spacer lengths that increase the predicted epitope recovery rate fivefold while reducing the immunogenicity from neo-epitopes by 44 % compared to designs without spacers. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 84 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 28 | 33% |
| Student > Bachelor | 11 | 13% |
| Student > Ph. D. Student | 9 | 11% |
| Student > Master | 8 | 10% |
| Student > Postgraduate | 4 | 5% |
| Other | 10 | 12% |
| Unknown | 14 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 22 | 26% |
| Immunology and Microbiology | 16 | 19% |
| Agricultural and Biological Sciences | 11 | 13% |
| Computer Science | 4 | 5% |
| Chemistry | 4 | 5% |
| Other | 11 | 13% |
| Unknown | 16 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 18. 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 28 March 2024.
All research outputs
#2,074,564
of 25,738,558 outputs
Outputs from Genome Medicine
#458
of 1,611 outputs
Outputs of similar age
#35,166
of 407,944 outputs
Outputs of similar age from Genome Medicine
#9
of 33 outputs
Altmetric has tracked 25,738,558 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,611 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.5. This one has gotten more attention than average, scoring higher than 71% 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 407,944 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.