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Computational analysis to repurpose drugs for COVID-19 based on transcriptional response of host cells to SARS-CoV-2

Overview of attention for article published in BMC Medical Informatics and Decision Making, January 2021
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (64th percentile)
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

Mentioned by

twitter
7 X users

Citations

dimensions_citation
21 Dimensions

Readers on

mendeley
16 Mendeley
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Title
Computational analysis to repurpose drugs for COVID-19 based on transcriptional response of host cells to SARS-CoV-2
Published in
BMC Medical Informatics and Decision Making, January 2021
DOI 10.1186/s12911-020-01373-x
Pubmed ID
Authors

Fuhai Li, Andrew P. Michelson, Randi Foraker, Ming Zhan, Philip R. O. Payne

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 69%
Researcher 9 56%
Student > Master 8 50%
Student > Bachelor 7 44%
Professor 5 31%
Other 23 144%
Readers by discipline Count As %
Medicine and Dentistry 16 100%
Nursing and Health Professions 11 69%
Biochemistry, Genetics and Molecular Biology 9 56%
Pharmacology, Toxicology and Pharmaceutical Science 8 50%
Agricultural and Biological Sciences 4 25%
Other 15 94%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 11 January 2021.
All research outputs
#7,301,818
of 23,271,751 outputs
Outputs from BMC Medical Informatics and Decision Making
#726
of 2,022 outputs
Outputs of similar age
#179,747
of 503,269 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#22
of 63 outputs
Altmetric has tracked 23,271,751 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 2,022 research outputs from this source. They receive a mean Attention Score of 4.9. 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 503,269 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 64% of its contemporaries.
We're also able to compare this research output to 63 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 65% of its contemporaries.