Statistical method of context evaluation for biological sequence similarity
- Autores
- Lyroudia, Kleoniki; Bogan-Marta, Alina; Pitas, Ioannis
- Año de publicación
- 2006
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- 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.
IFIP International Conference on Artificial Intelligence in Theory and Practice - Integration of AI with other Technologies
Red de Universidades con Carreras en Informática (RedUNCI) - Materia
-
Ciencias Informáticas
Similarity measures
Linguistics - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/23873
Ver los metadatos del registro completo
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Statistical method of context evaluation for biological sequence similarityLyroudia, KleonikiBogan-Marta, AlinaPitas, IoannisCiencias InformáticasSimilarity measuresLinguisticsWithin 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.IFIP International Conference on Artificial Intelligence in Theory and Practice - Integration of AI with other TechnologiesRed de Universidades con Carreras en Informática (RedUNCI)2006-08info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/23873enginfo:eu-repo/semantics/altIdentifier/isbn/0-387-34654-6info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:55:36Zoai:sedici.unlp.edu.ar:10915/23873Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:36.727SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Statistical method of context evaluation for biological sequence similarity |
title |
Statistical method of context evaluation for biological sequence similarity |
spellingShingle |
Statistical method of context evaluation for biological sequence similarity Lyroudia, Kleoniki Ciencias Informáticas Similarity measures Linguistics |
title_short |
Statistical method of context evaluation for biological sequence similarity |
title_full |
Statistical method of context evaluation for biological sequence similarity |
title_fullStr |
Statistical method of context evaluation for biological sequence similarity |
title_full_unstemmed |
Statistical method of context evaluation for biological sequence similarity |
title_sort |
Statistical method of context evaluation for biological sequence similarity |
dc.creator.none.fl_str_mv |
Lyroudia, Kleoniki Bogan-Marta, Alina Pitas, Ioannis |
author |
Lyroudia, Kleoniki |
author_facet |
Lyroudia, Kleoniki Bogan-Marta, Alina Pitas, Ioannis |
author_role |
author |
author2 |
Bogan-Marta, Alina Pitas, Ioannis |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas Similarity measures Linguistics |
topic |
Ciencias Informáticas Similarity measures Linguistics |
dc.description.none.fl_txt_mv |
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. IFIP International Conference on Artificial Intelligence in Theory and Practice - Integration of AI with other Technologies Red de Universidades con Carreras en Informática (RedUNCI) |
description |
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. |
publishDate |
2006 |
dc.date.none.fl_str_mv |
2006-08 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
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conferenceObject |
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publishedVersion |
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http://sedici.unlp.edu.ar/handle/10915/23873 |
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eng |
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eng |
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info:eu-repo/semantics/altIdentifier/isbn/0-387-34654-6 |
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info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
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