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
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/23873

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network_name_str SEDICI (UNLP)
spelling 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
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dc.language.none.fl_str_mv eng
language eng
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dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
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