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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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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 49 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 43 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 24%
Student > Bachelor 8 16%
Student > Ph. D. Student 6 12%
Student > Doctoral Student 5 10%
Professor > Associate Professor 5 10%
Other 11 22%
Unknown 2 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 31%
Computer Science 9 18%
Social Sciences 6 12%
Biochemistry, Genetics and Molecular Biology 6 12%
Engineering 2 4%
Other 7 14%
Unknown 4 8%