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Computational models of liver fibrosis progression for hepatitis C virus chronic infection

Overview of attention for article published in BMC Bioinformatics, July 2014
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (76th percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

Mentioned by

blogs
1 blog

Citations

dimensions_citation
19 Dimensions

Readers on

mendeley
56 Mendeley
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Title
Computational models of liver fibrosis progression for hepatitis C virus chronic infection
Published in
BMC Bioinformatics, July 2014
DOI 10.1186/1471-2105-15-s8-s5
Pubmed ID
Authors

James Lara, F Xavier López-Labrador, Fernando González-Candelas, Marina Berenguer, Yury E Khudyakov

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 2%
Unknown 55 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 16%
Student > Master 7 13%
Other 6 11%
Student > Ph. D. Student 6 11%
Student > Bachelor 5 9%
Other 11 20%
Unknown 12 21%
Readers by discipline Count As %
Medicine and Dentistry 18 32%
Agricultural and Biological Sciences 5 9%
Engineering 4 7%
Computer Science 3 5%
Immunology and Microbiology 3 5%
Other 5 9%
Unknown 18 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 17 September 2020.
All research outputs
#5,870,333
of 23,237,082 outputs
Outputs from BMC Bioinformatics
#2,175
of 7,360 outputs
Outputs of similar age
#53,982
of 227,896 outputs
Outputs of similar age from BMC Bioinformatics
#41
of 131 outputs
Altmetric has tracked 23,237,082 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 7,360 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 70% 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 227,896 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 76% of its contemporaries.
We're also able to compare this research output to 131 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 68% of its contemporaries.