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Mendeley readers
| Title |
Probabilistic grammatical model for helix‐helix contact site classification
|
|---|---|
| Published in |
Algorithms for Molecular Biology, December 2013
|
| DOI | 10.1186/1748-7188-8-31 |
| Pubmed ID | |
| Authors |
Witold Dyrka, Jean‐Christophe Nebel, Malgorzata Kotulska |
| Abstract |
Hidden Markov Models power many state-of-the-art tools in the field of protein bioinformatics. While excelling in their tasks, these methods of protein analysis do not convey directly information on medium- and long-range residue-residue interactions. This requires an expressive power of at least context-free grammars. However, application of more powerful grammar formalisms to protein analysis has been surprisingly limited. |
Mendeley readers
The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 4% |
| Unknown | 27 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 32% |
| Researcher | 5 | 18% |
| Professor > Associate Professor | 3 | 11% |
| Other | 2 | 7% |
| Student > Master | 2 | 7% |
| Other | 4 | 14% |
| Unknown | 3 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 5 | 18% |
| Engineering | 4 | 14% |
| Biochemistry, Genetics and Molecular Biology | 4 | 14% |
| Agricultural and Biological Sciences | 4 | 14% |
| Linguistics | 1 | 4% |
| Other | 5 | 18% |
| Unknown | 5 | 18% |