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MimoSA: a system for minimotif annotation

Overview of attention for article published in BMC Bioinformatics, June 2010
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

twitter
1 tweeter
wikipedia
1 Wikipedia page

Citations

dimensions_citation
6 Dimensions

Readers on

mendeley
22 Mendeley
citeulike
1 CiteULike
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Title
MimoSA: a system for minimotif annotation
Published in
BMC Bioinformatics, June 2010
DOI 10.1186/1471-2105-11-328
Pubmed ID
Authors

Jay Vyas, Ronald J Nowling, Thomas Meusburger, David Sargeant, Krishna Kadaveru, Michael R Gryk, Vamsi Kundeti, Sanguthevar Rajasekaran, Martin R Schiller

Abstract

Minimotifs are short peptide sequences within one protein, which are recognized by other proteins or molecules. While there are now several minimotif databases, they are incomplete. There are reports of many minimotifs in the primary literature, which have yet to be annotated, while entirely novel minimotifs continue to be published on a weekly basis. Our recently proposed function and sequence syntax for minimotifs enables us to build a general tool that will facilitate structured annotation and management of minimotif data from the biomedical literature.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 9%
United Kingdom 1 5%
France 1 5%
Unknown 18 82%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 27%
Student > Ph. D. Student 5 23%
Student > Master 4 18%
Student > Bachelor 3 14%
Professor 1 5%
Other 1 5%
Unknown 2 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 36%
Computer Science 4 18%
Engineering 3 14%
Medicine and Dentistry 1 5%
Materials Science 1 5%
Other 1 5%
Unknown 4 18%

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 12 August 2019.
All research outputs
#4,625,392
of 16,650,496 outputs
Outputs from BMC Bioinformatics
#1,938
of 5,986 outputs
Outputs of similar age
#74,868
of 295,311 outputs
Outputs of similar age from BMC Bioinformatics
#11
of 40 outputs
Altmetric has tracked 16,650,496 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 5,986 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has gotten more attention than average, scoring higher than 66% 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 295,311 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 73% of its contemporaries.
We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.