↓ Skip to main content

Application of text-mining for updating protein post-translational modification annotation in UniProtKB

Overview of attention for article published in BMC Bioinformatics, March 2013
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (67th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
4 tweeters

Citations

dimensions_citation
15 Dimensions

Readers on

mendeley
64 Mendeley
citeulike
5 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Application of text-mining for updating protein post-translational modification annotation in UniProtKB
Published in
BMC Bioinformatics, March 2013
DOI 10.1186/1471-2105-14-104
Pubmed ID
Authors

Anne-Lise Veuthey, Alan Bridge, Julien Gobeill, Patrick Ruch, Johanna R McEntyre, Lydie Bougueleret, Ioannis Xenarios

Abstract

The annotation of protein post-translational modifications (PTMs) is an important task of UniProtKB curators and, with continuing improvements in experimental methodology, an ever greater number of articles are being published on this topic. To help curators cope with this growing body of information we have developed a system which extracts information from the scientific literature for the most frequently annotated PTMs in UniProtKB.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 2 3%
Germany 1 2%
Australia 1 2%
France 1 2%
India 1 2%
South Africa 1 2%
Unknown 57 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 28%
Student > Master 8 13%
Student > Ph. D. Student 8 13%
Student > Bachelor 5 8%
Professor > Associate Professor 4 6%
Other 10 16%
Unknown 11 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 31%
Biochemistry, Genetics and Molecular Biology 11 17%
Computer Science 11 17%
Engineering 2 3%
Medicine and Dentistry 2 3%
Other 6 9%
Unknown 12 19%

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 08 April 2013.
All research outputs
#6,696,898
of 21,344,814 outputs
Outputs from BMC Bioinformatics
#2,715
of 6,923 outputs
Outputs of similar age
#54,465
of 173,813 outputs
Outputs of similar age from BMC Bioinformatics
#16
of 30 outputs
Altmetric has tracked 21,344,814 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 6,923 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 59% 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 173,813 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 67% of its contemporaries.
We're also able to compare this research output to 30 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 50% of its contemporaries.