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A stochastic context free grammar based framework for analysis of protein sequences

Overview of attention for article published in BMC Bioinformatics, October 2009
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (59th percentile)

Mentioned by

wikipedia
1 Wikipedia page

Citations

dimensions_citation
21 Dimensions

Readers on

mendeley
48 Mendeley
citeulike
3 CiteULike
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Title
A stochastic context free grammar based framework for analysis of protein sequences
Published in
BMC Bioinformatics, October 2009
DOI 10.1186/1471-2105-10-323
Pubmed ID
Authors

Witold Dyrka, Jean-Christophe Nebel

Abstract

In the last decade, there have been many applications of formal language theory in bioinformatics such as RNA structure prediction and detection of patterns in DNA. However, in the field of proteomics, the size of the protein alphabet and the complexity of relationship between amino acids have mainly limited the application of formal language theory to the production of grammars whose expressive power is not higher than stochastic regular grammars. However, these grammars, like other state of the art methods, cannot cover any higher-order dependencies such as nested and crossing relationships that are common in proteins. In order to overcome some of these limitations, we propose a Stochastic Context Free Grammar based framework for the analysis of protein sequences where grammars are induced using a genetic algorithm.

Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 4%
United Kingdom 1 2%
Netherlands 1 2%
Thailand 1 2%
United States 1 2%
Unknown 42 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 23%
Student > Bachelor 8 17%
Student > Ph. D. Student 6 13%
Student > Doctoral Student 5 10%
Professor > Associate Professor 5 10%
Other 11 23%
Unknown 2 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 31%
Computer Science 9 19%
Social Sciences 6 13%
Biochemistry, Genetics and Molecular Biology 5 10%
Engineering 2 4%
Other 7 15%
Unknown 4 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 24 November 2014.
All research outputs
#1,049,382
of 4,545,034 outputs
Outputs from BMC Bioinformatics
#977
of 2,660 outputs
Outputs of similar age
#43,762
of 151,457 outputs
Outputs of similar age from BMC Bioinformatics
#46
of 122 outputs
Altmetric has tracked 4,545,034 research outputs across all sources so far. This one has received more attention than most of these and is in the 64th percentile.
So far Altmetric has tracked 2,660 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 56% 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 151,457 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 69% of its contemporaries.
We're also able to compare this research output to 122 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 59% of its contemporaries.