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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
  • High Attention Score compared to outputs of the same age (84th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

Mentioned by

blogs
1 blog
twitter
9 X users
patent
1 patent
facebook
1 Facebook page

Citations

dimensions_citation
80 Dimensions

Readers on

mendeley
170 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

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 170 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 170 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 31 18%
Student > Bachelor 24 14%
Student > Ph. D. Student 16 9%
Other 14 8%
Student > Master 12 7%
Other 21 12%
Unknown 52 31%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 24 14%
Medicine and Dentistry 22 13%
Immunology and Microbiology 16 9%
Nursing and Health Professions 10 6%
Agricultural and Biological Sciences 10 6%
Other 34 20%
Unknown 54 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 April 2023.
All research outputs
#2,337,073
of 24,589,002 outputs
Outputs from Genome Medicine
#531
of 1,516 outputs
Outputs of similar age
#60,487
of 403,492 outputs
Outputs of similar age from Genome Medicine
#11
of 24 outputs
Altmetric has tracked 24,589,002 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,516 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.2. This one has gotten more attention than average, scoring higher than 65% 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 403,492 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 24 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 58% of its contemporaries.