Biclustering on expression data: A review

Biclustering has become a popular technique for the study of gene expression data, especially for discovering functionally related gene sets under different subsets of experimental conditions. Most of biclustering approaches use a measure or cost function that determines the quality of biclusters. I...

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Detalhes bibliográficos
Autores: Pontes Balanza, Beatriz, Giráldez, Raúl, Aguilar Ruiz, Jesús Salvador
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2015
País:España
Recursos:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/130192
Acesso em linha:https://hdl.handle.net/11441/130192
https://doi.org/10.1016/j.jbi.2015.06.028
Access Level:acceso abierto
Palavra-chave:Microarray analysis
Gene Expression Data
Biclustering techniques
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spelling Biclustering on expression data: A reviewPontes Balanza, BeatrizGiráldez, RaúlAguilar Ruiz, Jesús SalvadorMicroarray analysisGene Expression DataBiclustering techniquesBiclustering has become a popular technique for the study of gene expression data, especially for discovering functionally related gene sets under different subsets of experimental conditions. Most of biclustering approaches use a measure or cost function that determines the quality of biclusters. In such cases, the development of both a suitable heuristics and a good measure for guiding the search are essential for discovering interesting biclusters in an expression matrix. Nevertheless, not all existing biclustering approaches base their search on evaluation measures for biclusters. There exists a diverse set of biclustering tools that follow different strategies and algorithmic concepts which guide the search towards meaningful results. In this paper we present a extensive survey of biclustering approaches, classifying them into two categories according to whether or not use evaluation metrics within the search method: biclustering algorithms based on evaluation measures and non metric-based biclustering algorithms. In both cases, they have been classified according to the type of meta-heuristics which they are based on.Ministerio de Economía y Competitividad TIN2011-28956ElsevierLenguajes y Sistemas InformáticosMinisterio de Economía y Competitividad (MINECO). España2015info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/130192https://doi.org/10.1016/j.jbi.2015.06.028reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésJournal of Biomedical Informatics, 57 (October 2015), 163-180.TIN2011-28956https://www.sciencedirect.com/science/article/pii/S1532046415001380info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1301922026-06-17T12:51:07Z
dc.title.none.fl_str_mv Biclustering on expression data: A review
title Biclustering on expression data: A review
spellingShingle Biclustering on expression data: A review
Pontes Balanza, Beatriz
Microarray analysis
Gene Expression Data
Biclustering techniques
title_short Biclustering on expression data: A review
title_full Biclustering on expression data: A review
title_fullStr Biclustering on expression data: A review
title_full_unstemmed Biclustering on expression data: A review
title_sort Biclustering on expression data: A review
dc.creator.none.fl_str_mv Pontes Balanza, Beatriz
Giráldez, Raúl
Aguilar Ruiz, Jesús Salvador
author Pontes Balanza, Beatriz
author_facet Pontes Balanza, Beatriz
Giráldez, Raúl
Aguilar Ruiz, Jesús Salvador
author_role author
author2 Giráldez, Raúl
Aguilar Ruiz, Jesús Salvador
author2_role author
author
dc.contributor.none.fl_str_mv Lenguajes y Sistemas Informáticos
Ministerio de Economía y Competitividad (MINECO). España
dc.subject.none.fl_str_mv Microarray analysis
Gene Expression Data
Biclustering techniques
topic Microarray analysis
Gene Expression Data
Biclustering techniques
description Biclustering has become a popular technique for the study of gene expression data, especially for discovering functionally related gene sets under different subsets of experimental conditions. Most of biclustering approaches use a measure or cost function that determines the quality of biclusters. In such cases, the development of both a suitable heuristics and a good measure for guiding the search are essential for discovering interesting biclusters in an expression matrix. Nevertheless, not all existing biclustering approaches base their search on evaluation measures for biclusters. There exists a diverse set of biclustering tools that follow different strategies and algorithmic concepts which guide the search towards meaningful results. In this paper we present a extensive survey of biclustering approaches, classifying them into two categories according to whether or not use evaluation metrics within the search method: biclustering algorithms based on evaluation measures and non metric-based biclustering algorithms. In both cases, they have been classified according to the type of meta-heuristics which they are based on.
publishDate 2015
dc.date.none.fl_str_mv 2015
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/130192
https://doi.org/10.1016/j.jbi.2015.06.028
url https://hdl.handle.net/11441/130192
https://doi.org/10.1016/j.jbi.2015.06.028
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Journal of Biomedical Informatics, 57 (October 2015), 163-180.
TIN2011-28956
https://www.sciencedirect.com/science/article/pii/S1532046415001380
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300719

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