A feature selection approach for evaluate the inference of GRNs through biological data integration-A case study on A. Thaliana

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dc.contributor.authorVicente, Fábio F.R.
dc.contributor.authorMenezes, Euler
dc.contributor.authorRubino, Gabriel
dc.contributor.authorDe Oliveira, Juliana [UNESP]
dc.contributor.authorLopes, Fabrício Martins
dc.contributor.institutionFederal University of Technology
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-12-11T17:05:11Z
dc.date.available2018-12-11T17:05:11Z
dc.date.issued2015-01-01
dc.description.abstractThe 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.en
dc.description.affiliationFederal University of Technology
dc.description.affiliationInstitute of Mathematics and Statistics University of São Paulo
dc.description.affiliationDepartment of Biological Sciences Faculty of Sciences and Letters of Assis-FCLA University of São Paulo State-UNESP, Av. Dom Antonio, 2100, Parque Universitrio
dc.description.affiliationUnespDepartment of Biological Sciences Faculty of Sciences and Letters of Assis-FCLA University of São Paulo State-UNESP, Av. Dom Antonio, 2100, Parque Universitrio
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipFundação Araucária
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipUniversidade de São Paulo
dc.description.sponsorshipIdFAPESP: 2011/50761-2
dc.format.extent667-675
dc.identifierhttp://dx.doi.org/10.1007/978-3-319-25751-8_80
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9423, p. 667-675.
dc.identifier.doi10.1007/978-3-319-25751-8_80
dc.identifier.issn1611-3349
dc.identifier.issn0302-9743
dc.identifier.scopus2-s2.0-84983628497
dc.identifier.urihttp://hdl.handle.net/11449/173410
dc.language.isoeng
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartofsjr0,295
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectArabidopsis thaliana
dc.subjectBioinformatics
dc.subjectData integration
dc.subjectFeature selection
dc.subjectGene regulatory networks
dc.titleA feature selection approach for evaluate the inference of GRNs through biological data integration-A case study on A. Thalianaen
dc.typeTrabalho apresentado em evento

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Artigos - Ciências Biológicas - FCLAS