Hybridizing sparse component analysis with genetic algorithms for microarray analysis
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10773/5819 |
Resumo: | Nonnegative Matrix Factorization (NMF) has proven to be a useful tool for the analysis of nonnegative multivariate data. However, it is known not to lead to unique results when applied to Blind Source Separation (BSS) problems. In this paper we present an extension of NMF capable of solving the BSS problem when the underlying sources are sufficiently sparse. In contrast to most well-established BSS methods, the devised algorithm is capable of solving the BSS problem in cases where the underlying sources are not independent or uncorrelated. As the proposed fitness function is discontinuous and possesses many local minima, we use a genetic algorithm for its minimization. Finally, we apply the devised algorithm to real world microarray data. |
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Hybridizing sparse component analysis with genetic algorithms for microarray analysisSparse nonnegative matrix factorizationBlind source separationGene microarray analysisNonnegative Matrix Factorization (NMF) has proven to be a useful tool for the analysis of nonnegative multivariate data. However, it is known not to lead to unique results when applied to Blind Source Separation (BSS) problems. In this paper we present an extension of NMF capable of solving the BSS problem when the underlying sources are sufficiently sparse. In contrast to most well-established BSS methods, the devised algorithm is capable of solving the BSS problem in cases where the underlying sources are not independent or uncorrelated. As the proposed fitness function is discontinuous and possesses many local minima, we use a genetic algorithm for its minimization. Finally, we apply the devised algorithm to real world microarray data.Elsevier2012-02-06T12:48:25Z2008-06-01T00:00:00Z2008-06info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/5819eng0925-231210.1016/j.neucom.2007.09.017Stadlthanner, K.Theis, F. J.Lang, E. W.Tomé, A. M.Puntonet, C. G.Górriz, J. M.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-22T11:08:28Zoai:ria.ua.pt:10773/5819Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T02:43:34.763985Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Hybridizing sparse component analysis with genetic algorithms for microarray analysis |
title |
Hybridizing sparse component analysis with genetic algorithms for microarray analysis |
spellingShingle |
Hybridizing sparse component analysis with genetic algorithms for microarray analysis Stadlthanner, K. Sparse nonnegative matrix factorization Blind source separation Gene microarray analysis |
title_short |
Hybridizing sparse component analysis with genetic algorithms for microarray analysis |
title_full |
Hybridizing sparse component analysis with genetic algorithms for microarray analysis |
title_fullStr |
Hybridizing sparse component analysis with genetic algorithms for microarray analysis |
title_full_unstemmed |
Hybridizing sparse component analysis with genetic algorithms for microarray analysis |
title_sort |
Hybridizing sparse component analysis with genetic algorithms for microarray analysis |
author |
Stadlthanner, K. |
author_facet |
Stadlthanner, K. Theis, F. J. Lang, E. W. Tomé, A. M. Puntonet, C. G. Górriz, J. M. |
author_role |
author |
author2 |
Theis, F. J. Lang, E. W. Tomé, A. M. Puntonet, C. G. Górriz, J. M. |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Stadlthanner, K. Theis, F. J. Lang, E. W. Tomé, A. M. Puntonet, C. G. Górriz, J. M. |
dc.subject.por.fl_str_mv |
Sparse nonnegative matrix factorization Blind source separation Gene microarray analysis |
topic |
Sparse nonnegative matrix factorization Blind source separation Gene microarray analysis |
description |
Nonnegative Matrix Factorization (NMF) has proven to be a useful tool for the analysis of nonnegative multivariate data. However, it is known not to lead to unique results when applied to Blind Source Separation (BSS) problems. In this paper we present an extension of NMF capable of solving the BSS problem when the underlying sources are sufficiently sparse. In contrast to most well-established BSS methods, the devised algorithm is capable of solving the BSS problem in cases where the underlying sources are not independent or uncorrelated. As the proposed fitness function is discontinuous and possesses many local minima, we use a genetic algorithm for its minimization. Finally, we apply the devised algorithm to real world microarray data. |
publishDate |
2008 |
dc.date.none.fl_str_mv |
2008-06-01T00:00:00Z 2008-06 2012-02-06T12:48:25Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10773/5819 |
url |
http://hdl.handle.net/10773/5819 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0925-2312 10.1016/j.neucom.2007.09.017 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799137486226587648 |