A feature selection approach for evaluate the inference of GRNs through biological data integration-A case study on A. Thaliana
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/978-3-319-25751-8_80 http://hdl.handle.net/11449/173410 |
Resumo: | The inference of gene regulatory networks (GRNs) from expression profiles is a great challenge in bioinformatics due to the curse of dimensionality. For this reason, several methods that perform data integration have been developed to reduce the estimation error of the inference. However, it is not completely formulated how to use each type of biological information available. This work address this issue by proposing feature selection approach in order to integrate biological data and evaluate three types of biological information regarding their effect on the similarity of inferred GRNs. The proposed feature selection method is based on sequential forward floating selection (SFFS) search algorithm and the mean conditional entropy (MCE) as criterion function. An expression dataset was built as an additional contribution of this work containing 22746 genes and 1206 experiments regarding A. thaliana. The experimental results achieve 39% of GRNs improvement in average when compared to non-use of biological data integration. Besides, the results showed that the improvement is associated to a specific type of biological information: the cellular localization, which is a valuable and information for the development of new experiments and indicates an important insight for investigation. |
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A feature selection approach for evaluate the inference of GRNs through biological data integration-A case study on A. ThalianaArabidopsis thalianaBioinformaticsData integrationFeature selectionGene regulatory networksThe inference of gene regulatory networks (GRNs) from expression profiles is a great challenge in bioinformatics due to the curse of dimensionality. For this reason, several methods that perform data integration have been developed to reduce the estimation error of the inference. However, it is not completely formulated how to use each type of biological information available. This work address this issue by proposing feature selection approach in order to integrate biological data and evaluate three types of biological information regarding their effect on the similarity of inferred GRNs. The proposed feature selection method is based on sequential forward floating selection (SFFS) search algorithm and the mean conditional entropy (MCE) as criterion function. An expression dataset was built as an additional contribution of this work containing 22746 genes and 1206 experiments regarding A. thaliana. The experimental results achieve 39% of GRNs improvement in average when compared to non-use of biological data integration. Besides, the results showed that the improvement is associated to a specific type of biological information: the cellular localization, which is a valuable and information for the development of new experiments and indicates an important insight for investigation.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação AraucáriaFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Universidade de São PauloFederal University of TechnologyInstitute of Mathematics and Statistics University of São PauloDepartment of Biological Sciences Faculty of Sciences and Letters of Assis-FCLA University of São Paulo State-UNESP, Av. Dom Antonio, 2100, Parque UniversitrioDepartment of Biological Sciences Faculty of Sciences and Letters of Assis-FCLA University of São Paulo State-UNESP, Av. Dom Antonio, 2100, Parque UniversitrioFAPESP: 2011/50761-2Federal University of TechnologyUniversidade de São Paulo (USP)Universidade Estadual Paulista (Unesp)Vicente, Fábio F.R.Menezes, EulerRubino, GabrielDe Oliveira, Juliana [UNESP]Lopes, Fabrício Martins2018-12-11T17:05:11Z2018-12-11T17:05:11Z2015-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject667-675http://dx.doi.org/10.1007/978-3-319-25751-8_80Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9423, p. 667-675.1611-33490302-9743http://hdl.handle.net/11449/17341010.1007/978-3-319-25751-8_802-s2.0-84983628497Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)0,295info:eu-repo/semantics/openAccess2021-10-23T21:44:27Zoai:repositorio.unesp.br:11449/173410Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T21:44:27Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A feature selection approach for evaluate the inference of GRNs through biological data integration-A case study on A. Thaliana |
title |
A feature selection approach for evaluate the inference of GRNs through biological data integration-A case study on A. Thaliana |
spellingShingle |
A feature selection approach for evaluate the inference of GRNs through biological data integration-A case study on A. Thaliana Vicente, Fábio F.R. Arabidopsis thaliana Bioinformatics Data integration Feature selection Gene regulatory networks |
title_short |
A feature selection approach for evaluate the inference of GRNs through biological data integration-A case study on A. Thaliana |
title_full |
A feature selection approach for evaluate the inference of GRNs through biological data integration-A case study on A. Thaliana |
title_fullStr |
A feature selection approach for evaluate the inference of GRNs through biological data integration-A case study on A. Thaliana |
title_full_unstemmed |
A feature selection approach for evaluate the inference of GRNs through biological data integration-A case study on A. Thaliana |
title_sort |
A feature selection approach for evaluate the inference of GRNs through biological data integration-A case study on A. Thaliana |
author |
Vicente, Fábio F.R. |
author_facet |
Vicente, Fábio F.R. Menezes, Euler Rubino, Gabriel De Oliveira, Juliana [UNESP] Lopes, Fabrício Martins |
author_role |
author |
author2 |
Menezes, Euler Rubino, Gabriel De Oliveira, Juliana [UNESP] Lopes, Fabrício Martins |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Federal University of Technology Universidade de São Paulo (USP) Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Vicente, Fábio F.R. Menezes, Euler Rubino, Gabriel De Oliveira, Juliana [UNESP] Lopes, Fabrício Martins |
dc.subject.por.fl_str_mv |
Arabidopsis thaliana Bioinformatics Data integration Feature selection Gene regulatory networks |
topic |
Arabidopsis thaliana Bioinformatics Data integration Feature selection Gene regulatory networks |
description |
The inference of gene regulatory networks (GRNs) from expression profiles is a great challenge in bioinformatics due to the curse of dimensionality. For this reason, several methods that perform data integration have been developed to reduce the estimation error of the inference. However, it is not completely formulated how to use each type of biological information available. This work address this issue by proposing feature selection approach in order to integrate biological data and evaluate three types of biological information regarding their effect on the similarity of inferred GRNs. The proposed feature selection method is based on sequential forward floating selection (SFFS) search algorithm and the mean conditional entropy (MCE) as criterion function. An expression dataset was built as an additional contribution of this work containing 22746 genes and 1206 experiments regarding A. thaliana. The experimental results achieve 39% of GRNs improvement in average when compared to non-use of biological data integration. Besides, the results showed that the improvement is associated to a specific type of biological information: the cellular localization, which is a valuable and information for the development of new experiments and indicates an important insight for investigation. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-01-01 2018-12-11T17:05:11Z 2018-12-11T17:05:11Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/978-3-319-25751-8_80 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9423, p. 667-675. 1611-3349 0302-9743 http://hdl.handle.net/11449/173410 10.1007/978-3-319-25751-8_80 2-s2.0-84983628497 |
url |
http://dx.doi.org/10.1007/978-3-319-25751-8_80 http://hdl.handle.net/11449/173410 |
identifier_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9423, p. 667-675. 1611-3349 0302-9743 10.1007/978-3-319-25751-8_80 2-s2.0-84983628497 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 0,295 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
667-675 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
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1799964965627494400 |