Title |
A stochastic context free grammar based framework for analysis of protein sequences
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Published in |
BMC Bioinformatics, October 2009
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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
Geographical breakdown
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Brazil | 2 | 4% |
Netherlands | 1 | 2% |
United Kingdom | 1 | 2% |
Thailand | 1 | 2% |
United States | 1 | 2% |
Unknown | 50 | 89% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 13 | 23% |
Student > Ph. D. Student | 8 | 14% |
Student > Bachelor | 8 | 14% |
Student > Doctoral Student | 5 | 9% |
Professor > Associate Professor | 4 | 7% |
Other | 14 | 25% |
Unknown | 4 | 7% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 15 | 27% |
Computer Science | 9 | 16% |
Biochemistry, Genetics and Molecular Biology | 7 | 13% |
Social Sciences | 6 | 11% |
Engineering | 3 | 5% |
Other | 9 | 16% |
Unknown | 7 | 13% |