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Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes

Overview of attention for article published in Genome Medicine, August 2020
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (71st percentile)

Mentioned by

twitter
8 tweeters
patent
1 patent
facebook
1 Facebook page

Citations

dimensions_citation
58 Dimensions

Readers on

mendeley
153 Mendeley
Title
Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes
Published in
Genome Medicine, August 2020
DOI 10.1186/s13073-020-00767-w
Pubmed ID
Authors

Asaf Poran, Dewi Harjanto, Matthew Malloy, Christina M. Arieta, Daniel A. Rothenberg, Divya Lenkala, Marit M. van Buuren, Terri A. Addona, Michael S. Rooney, Lakshmi Srinivasan, Richard B. Gaynor

Twitter Demographics

The data shown below were collected from the profiles of 8 tweeters 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 153 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 153 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 25 16%
Student > Bachelor 23 15%
Other 15 10%
Student > Ph. D. Student 15 10%
Student > Master 8 5%
Other 21 14%
Unknown 46 30%
Readers by discipline Count As %
Medicine and Dentistry 21 14%
Biochemistry, Genetics and Molecular Biology 19 12%
Immunology and Microbiology 13 8%
Nursing and Health Professions 10 7%
Agricultural and Biological Sciences 7 5%
Other 36 24%
Unknown 47 31%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 24 February 2022.
All research outputs
#4,066,464
of 21,262,134 outputs
Outputs from Genome Medicine
#787
of 1,353 outputs
Outputs of similar age
#89,155
of 313,336 outputs
Outputs of similar age from Genome Medicine
#1
of 1 outputs
Altmetric has tracked 21,262,134 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,353 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.8. This one is in the 41st percentile – i.e., 41% 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 313,336 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 71% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them