Generalization of entropy based divergence measures for symbolic sequence analysis

Autores
Ré, Miguel A.; Azad, Rajeev K.
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Entropy based measures have been frequently used in symbolic sequence analysis. A symmetrized and smoothed form of Kullback-Leibler divergence or relative entropy, the Jensen-Shannon divergence (JSD), is of particular interest because of its sharing properties with families of other divergence measures and its interpretability in different domains including statistical physics, information theory and mathematical statistics. The uniqueness and versatility of this measure arise because of a number of attributes including generalization to any number of probability distributions and association of weights to the distributions. Furthermore, its entropic formulation allows its generalization in different statistical frameworks, such as, non-extensive Tsallis statistics and higher order Markovian statistics. We revisit these generalizations and propose a new generalization of JSD in the integrated Tsallis and Markovian statistical framework. We show that this generalization can be interpreted in terms of mutual information. We also investigate the performance of different JSD generalizations in deconstructing chimeric DNA sequences assembled from bacterial genomes including that of E. coli, S. enterica typhi, Y. pestis and H. influenzae. Our results show that the JSD generalizations bring in more pronounced improvements when the sequences being compared are from phylogenetically proximal organisms, which are often difficult to distinguish because of their compositional similarity. While small but noticeable improvements were observed with the Tsallis statistical JSD generalization, relatively large improvements were observed with the Markovian generalization. In contrast, the proposed Tsallis-Markovian generalization yielded more pronounced improvements relative to the Tsallis and Markovian generalizations, specifically when the sequences being compared arose from phylogenetically proximal organisms.
Fil: Ré, Miguel A. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Departamento Ciencias Básicas. Centro de Investigación en Informática para la Ingeniería; Argentina.
Fil: Azad, Rajeev K. University of North Texas. Department of Biological Sciences. Department of Mathematics; Unites States of America.
Fil: Ré, Miguel A. Universidad Nacional de Córdoba. Facultad de Matematica, Astronomía y Física; Argentina.
Peer Reviewed
Fuente
Plos one 9(4): 1-11 (2014).
Materia
Entropy
Symbolic Sequence Analysis
Nivel de accesibilidad
acceso abierto
Condiciones de uso
Attribution-NonCommercial-NoDerivatives 4.0 International
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/13924
Acceder
id RIAUTN_e52fa0197cc207e6492778b79ea7715a
oai_identifier_str oai:ria.utn.edu.ar:20.500.12272/13924
network_acronym_str RIAUTN
repository_id_str a
network_name_str Repositorio Institucional Abierto (UTN)
spelling Generalization of entropy based divergence measures for symbolic sequence analysisRé, Miguel A.Azad, Rajeev K.EntropySymbolic Sequence AnalysisEntropy based measures have been frequently used in symbolic sequence analysis. A symmetrized and smoothed form of Kullback-Leibler divergence or relative entropy, the Jensen-Shannon divergence (JSD), is of particular interest because of its sharing properties with families of other divergence measures and its interpretability in different domains including statistical physics, information theory and mathematical statistics. The uniqueness and versatility of this measure arise because of a number of attributes including generalization to any number of probability distributions and association of weights to the distributions. Furthermore, its entropic formulation allows its generalization in different statistical frameworks, such as, non-extensive Tsallis statistics and higher order Markovian statistics. We revisit these generalizations and propose a new generalization of JSD in the integrated Tsallis and Markovian statistical framework. We show that this generalization can be interpreted in terms of mutual information. We also investigate the performance of different JSD generalizations in deconstructing chimeric DNA sequences assembled from bacterial genomes including that of E. coli, S. enterica typhi, Y. pestis and H. influenzae. Our results show that the JSD generalizations bring in more pronounced improvements when the sequences being compared are from phylogenetically proximal organisms, which are often difficult to distinguish because of their compositional similarity. While small but noticeable improvements were observed with the Tsallis statistical JSD generalization, relatively large improvements were observed with the Markovian generalization. In contrast, the proposed Tsallis-Markovian generalization yielded more pronounced improvements relative to the Tsallis and Markovian generalizations, specifically when the sequences being compared arose from phylogenetically proximal organisms.Fil: Ré, Miguel A. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Departamento Ciencias Básicas. Centro de Investigación en Informática para la Ingeniería; Argentina.Fil: Azad, Rajeev K. University of North Texas. Department of Biological Sciences. Department of Mathematics; Unites States of America.Fil: Ré, Miguel A. Universidad Nacional de Córdoba. Facultad de Matematica, Astronomía y Física; Argentina.Peer ReviewedPublic Library of Science2025-10-08T18:17:50Z2014info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articulopdfapplication/pdfPlos onehttps://hdl.handle.net/20.500.12272/13924https://doi.org/10.1371/journal.pone.0093532Plos one 9(4): 1-11 (2014).reponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacionalenginfo:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Ré, Miguel; Azad, Rajeev K.https://creativecommons.org/licenses/by/4.0/2026-09-24T12:47:30Zoai:ria.utn.edu.ar:20.500.12272/13924instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2026-09-24 12:47:31.522Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv Generalization of entropy based divergence measures for symbolic sequence analysis
title Generalization of entropy based divergence measures for symbolic sequence analysis
spellingShingle Generalization of entropy based divergence measures for symbolic sequence analysis
Ré, Miguel A.
Entropy
Symbolic Sequence Analysis
title_short Generalization of entropy based divergence measures for symbolic sequence analysis
title_full Generalization of entropy based divergence measures for symbolic sequence analysis
title_fullStr Generalization of entropy based divergence measures for symbolic sequence analysis
title_full_unstemmed Generalization of entropy based divergence measures for symbolic sequence analysis
title_sort Generalization of entropy based divergence measures for symbolic sequence analysis
dc.creator.none.fl_str_mv Ré, Miguel A.
Azad, Rajeev K.
author Ré, Miguel A.
author_facet Ré, Miguel A.
Azad, Rajeev K.
author_role author
author2 Azad, Rajeev K.
author2_role author
dc.subject.none.fl_str_mv Entropy
Symbolic Sequence Analysis
topic Entropy
Symbolic Sequence Analysis
dc.description.none.fl_txt_mv Entropy based measures have been frequently used in symbolic sequence analysis. A symmetrized and smoothed form of Kullback-Leibler divergence or relative entropy, the Jensen-Shannon divergence (JSD), is of particular interest because of its sharing properties with families of other divergence measures and its interpretability in different domains including statistical physics, information theory and mathematical statistics. The uniqueness and versatility of this measure arise because of a number of attributes including generalization to any number of probability distributions and association of weights to the distributions. Furthermore, its entropic formulation allows its generalization in different statistical frameworks, such as, non-extensive Tsallis statistics and higher order Markovian statistics. We revisit these generalizations and propose a new generalization of JSD in the integrated Tsallis and Markovian statistical framework. We show that this generalization can be interpreted in terms of mutual information. We also investigate the performance of different JSD generalizations in deconstructing chimeric DNA sequences assembled from bacterial genomes including that of E. coli, S. enterica typhi, Y. pestis and H. influenzae. Our results show that the JSD generalizations bring in more pronounced improvements when the sequences being compared are from phylogenetically proximal organisms, which are often difficult to distinguish because of their compositional similarity. While small but noticeable improvements were observed with the Tsallis statistical JSD generalization, relatively large improvements were observed with the Markovian generalization. In contrast, the proposed Tsallis-Markovian generalization yielded more pronounced improvements relative to the Tsallis and Markovian generalizations, specifically when the sequences being compared arose from phylogenetically proximal organisms.
Fil: Ré, Miguel A. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Departamento Ciencias Básicas. Centro de Investigación en Informática para la Ingeniería; Argentina.
Fil: Azad, Rajeev K. University of North Texas. Department of Biological Sciences. Department of Mathematics; Unites States of America.
Fil: Ré, Miguel A. Universidad Nacional de Córdoba. Facultad de Matematica, Astronomía y Física; Argentina.
Peer Reviewed
description Entropy based measures have been frequently used in symbolic sequence analysis. A symmetrized and smoothed form of Kullback-Leibler divergence or relative entropy, the Jensen-Shannon divergence (JSD), is of particular interest because of its sharing properties with families of other divergence measures and its interpretability in different domains including statistical physics, information theory and mathematical statistics. The uniqueness and versatility of this measure arise because of a number of attributes including generalization to any number of probability distributions and association of weights to the distributions. Furthermore, its entropic formulation allows its generalization in different statistical frameworks, such as, non-extensive Tsallis statistics and higher order Markovian statistics. We revisit these generalizations and propose a new generalization of JSD in the integrated Tsallis and Markovian statistical framework. We show that this generalization can be interpreted in terms of mutual information. We also investigate the performance of different JSD generalizations in deconstructing chimeric DNA sequences assembled from bacterial genomes including that of E. coli, S. enterica typhi, Y. pestis and H. influenzae. Our results show that the JSD generalizations bring in more pronounced improvements when the sequences being compared are from phylogenetically proximal organisms, which are often difficult to distinguish because of their compositional similarity. While small but noticeable improvements were observed with the Tsallis statistical JSD generalization, relatively large improvements were observed with the Markovian generalization. In contrast, the proposed Tsallis-Markovian generalization yielded more pronounced improvements relative to the Tsallis and Markovian generalizations, specifically when the sequences being compared arose from phylogenetically proximal organisms.
publishDate 2014
dc.date.none.fl_str_mv 2014
2025-10-08T18:17:50Z
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv Plos one
https://hdl.handle.net/20.500.12272/13924
https://doi.org/10.1371/journal.pone.0093532
identifier_str_mv Plos one
url https://hdl.handle.net/20.500.12272/13924
https://doi.org/10.1371/journal.pone.0093532
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
Ré, Miguel; Azad, Rajeev K.
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
Ré, Miguel; Azad, Rajeev K.
https://creativecommons.org/licenses/by/4.0/
dc.format.none.fl_str_mv pdf
application/pdf
dc.publisher.none.fl_str_mv Public Library of Science
publisher.none.fl_str_mv Public Library of Science
dc.source.none.fl_str_mv Plos one 9(4): 1-11 (2014).
reponame:Repositorio Institucional Abierto (UTN)
instname:Universidad Tecnológica Nacional
reponame_str Repositorio Institucional Abierto (UTN)
collection Repositorio Institucional Abierto (UTN)
instname_str Universidad Tecnológica Nacional
repository.name.fl_str_mv Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional
repository.mail.fl_str_mv gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar
_version_ 1877230938177404928
score 13.24418

Publicaciones similares