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Potentially adaptive SARS-CoV-2 mutations discovered with novel spatiotemporal and explainable AI models

Overview of attention for article published in Genome Biology, December 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 (90th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

news
1 news outlet
twitter
17 X users

Citations

dimensions_citation
57 Dimensions

Readers on

mendeley
102 Mendeley
Title
Potentially adaptive SARS-CoV-2 mutations discovered with novel spatiotemporal and explainable AI models
Published in
Genome Biology, December 2020
DOI 10.1186/s13059-020-02191-0
Pubmed ID
Authors

Michael R. Garvin, Erica T. Prates, Mirko Pavicic, Piet Jones, B. Kirtley Amos, Armin Geiger, Manesh B. Shah, Jared Streich, Joao Gabriel Felipe Machado Gazolla, David Kainer, Ashley Cliff, Jonathon Romero, Nathan Keith, James B. Brown, Daniel Jacobson

X Demographics

X Demographics

The data shown below were collected from the profiles of 17 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 102 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 102 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 13 13%
Researcher 11 11%
Student > Ph. D. Student 10 10%
Student > Bachelor 7 7%
Student > Doctoral Student 6 6%
Other 21 21%
Unknown 34 33%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 15 15%
Medicine and Dentistry 10 10%
Computer Science 9 9%
Agricultural and Biological Sciences 7 7%
Social Sciences 3 3%
Other 20 20%
Unknown 38 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 19. 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 29 November 2023.
All research outputs
#1,925,293
of 25,387,668 outputs
Outputs from Genome Biology
#1,609
of 4,470 outputs
Outputs of similar age
#51,783
of 519,894 outputs
Outputs of similar age from Genome Biology
#51
of 88 outputs
Altmetric has tracked 25,387,668 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,470 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has gotten more attention than average, scoring higher than 63% 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 519,894 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 90% of its contemporaries.
We're also able to compare this research output to 88 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.