↓ Skip to main content

Clustering protein environments for function prediction: finding PROSITE motifs in 3D

Overview of attention for article published in BMC Bioinformatics, January 2007
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 (82nd percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

blogs
1 blog

Citations

dimensions_citation
21 Dimensions

Readers on

mendeley
29 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
Clustering protein environments for function prediction: finding PROSITE motifs in 3D
Published in
BMC Bioinformatics, January 2007
DOI 10.1186/1471-2105-8-s4-s10
Pubmed ID
Authors

Sungroh Yoon, Jessica C Ebert, Eui-Young Chung, Giovanni De Micheli, Russ B Altman

Abstract

Structural genomics initiatives are producing increasing numbers of three-dimensional (3D) structures for which there is little functional information. Structure-based annotation of molecular function is therefore becoming critical. We previously presented FEATURE, a method for describing microenvironments around functional sites in proteins. However, FEATURE uses supervised machine learning and so is limited to building models for sites of known importance and location. We hypothesized that there are a large number of sites in proteins that are associated with function that have not yet been recognized. Toward that end, we have developed a method for clustering protein microenvironments in order to evaluate the potential for discovering novel sites that have not been previously identified.

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 14%
Korea, Republic of 1 3%
Unknown 24 83%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 41%
Student > Ph. D. Student 8 28%
Professor > Associate Professor 3 10%
Student > Master 2 7%
Professor 2 7%
Other 1 3%
Unknown 1 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 31%
Computer Science 8 28%
Biochemistry, Genetics and Molecular Biology 3 10%
Engineering 2 7%
Medicine and Dentistry 2 7%
Other 4 14%
Unknown 1 3%

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 20 January 2009.
All research outputs
#527,047
of 3,684,317 outputs
Outputs from BMC Bioinformatics
#470
of 2,305 outputs
Outputs of similar age
#14,665
of 85,764 outputs
Outputs of similar age from BMC Bioinformatics
#20
of 128 outputs
Altmetric has tracked 3,684,317 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,305 research outputs from this source. They receive a mean Attention Score of 4.2. This one has done well, scoring higher than 79% 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 85,764 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 82% of its contemporaries.
We're also able to compare this research output to 128 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.