↓ Skip to main content

Predikin and PredikinDB: a computational framework for the prediction of protein kinase peptide specificity and an associated database of phosphorylation sites

Overview of attention for article published in BMC Bioinformatics, May 2008
Altmetric Badge

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 (86th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

blogs
1 blog

Citations

dimensions_citation
50 Dimensions

Readers on

mendeley
46 Mendeley
citeulike
3 CiteULike
connotea
1 Connotea
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
Predikin and PredikinDB: a computational framework for the prediction of protein kinase peptide specificity and an associated database of phosphorylation sites
Published in
BMC Bioinformatics, May 2008
DOI 10.1186/1471-2105-9-245
Pubmed ID
Authors

Neil FW Saunders, Ross I Brinkworth, Thomas Huber, Bruce E Kemp, Bostjan Kobe

Abstract

We have previously described an approach to predicting the substrate specificity of serine-threonine protein kinases. The method, named Predikin, identifies key conserved substrate-determining residues in the kinase catalytic domain that contact the substrate in the region of the phosphorylation site and so determine the sequence surrounding the phosphorylation site. Predikin was implemented originally as a web application written in Javascript.

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 2%
Brazil 1 2%
Argentina 1 2%
Spain 1 2%
United States 1 2%
Unknown 41 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 30%
Student > Ph. D. Student 13 28%
Professor 4 9%
Student > Doctoral Student 2 4%
Professor > Associate Professor 2 4%
Other 9 20%
Unknown 2 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 39%
Biochemistry, Genetics and Molecular Biology 10 22%
Medicine and Dentistry 5 11%
Chemistry 4 9%
Computer Science 3 7%
Other 3 7%
Unknown 3 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 03 June 2008.
All research outputs
#1,452,906
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#634
of 4,576 outputs
Outputs of similar age
#1,408,990
of 11,793,660 outputs
Outputs of similar age from BMC Bioinformatics
#634
of 4,580 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,576 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 85% 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 11,793,660 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 86% of its contemporaries.
We're also able to compare this research output to 4,580 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.