Statistical method of context evaluation for biological sequence similarity
Within this paper we are proposing and testing a new strategy for detection and measurement of similarity between sequences of proteins. Our approach has its roots in computational linguistics and the related techniques for quantifying and comparing content in strings of characters. The pairwise com...
Guardado en:
| Autores principales: | , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2006
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/23873 |
| Aporte de: |
| Sumario: | Within this paper we are proposing and testing a new strategy for detection and measurement of similarity between sequences of proteins. Our approach has its roots in computational linguistics and the related techniques for quantifying and comparing content in strings of characters. The pairwise comparison of proteins relies on the content regularities expected to uniquely characterize each sequence. These regularities are captured by n-gram based modelling techniques and exploited by cross-entropy related measures. In this new attempt to incorporate theoretical ideas from computational linguistics into the field of bioinformatics, we experimented using two implementations having always as ultimate goal the development of practical, computationally efficient algorithms for expressing protein similarity. The experimental analysis reported herein provides evidence for the usefulness of the proposed approach and motivates the further development of linguistics-related tools as a means of analysing biological sequences. |
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