In silico network topology-based prediction of gene essentiality

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dc.contributor.authorMuller da Silva, Joao Paulo [UNESP]
dc.contributor.authorAcencio, Marcio Luis [UNESP]
dc.contributor.authorMerino Mornbach, Jose Carlos
dc.contributor.authorVieira, Renata
dc.contributor.authorda Silva, Jose Camargo
dc.contributor.authorLemke, Ney [UNESP]
dc.contributor.authorSinigagliac, Marialva
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade Federal de Santa Maria (UFSM)
dc.contributor.institutionUniv Vale Rio dos Sinos
dc.date.accessioned2014-05-20T13:49:32Z
dc.date.available2014-05-20T13:49:32Z
dc.date.issued2008-02-01
dc.description.abstractThe identification of genes essential for survival is important for the understanding of the minimal requirements for cellular life and for drug design. As experimental studies with the purpose of building a catalog of essential genes for a given organism are time-consuming and laborious, a computational approach which could predict gene essentiality with high accuracy would be of great value. We present here a novel computational approach, called NTPGE (Network Topology-based Prediction of Gene Essentiality), that relies on the network topology features of a gene to estimate its essentiality. The first step of NTPGE is to construct the integrated molecular network for a given organism comprising protein physical, metabolic and transcriptional regulation interactions. The second step consists in training a decision-tree-based machine-learning algorithm on known essential and non-essential genes of the organism of interest, considering as learning attributes the network topology information for each of these genes. Finally, the decision-tree classifier generated is applied to the set of genes of this organism to estimate essentiality for each gene. We applied the NTPGE approach for discovering the essential genes in Escherichia coli and then assessed its performance. (C) 2007 Elsevier B.V. All rights reserved.en
dc.description.affiliationUNESP, Inst Biosci, Dept Phys & Biophys, BR-18618000 Botucatu, SP, Brazil
dc.description.affiliationUniversidade Federal de Santa Maria (UFSM), Ctr Ciencias Rurais, Unipampa Sao Gabriel Posgrad Fis, BR-97105900 Santa Maria, RS, Brazil
dc.description.affiliationUniv Vale Rio dos Sinos, Programs Interdisciplinar Computacao Aplicada, BR-93022000 Sao Leopoldo, RS, Brazil
dc.description.affiliationUnespUNESP, Inst Biosci, Dept Phys & Biophys, BR-18618000 Botucatu, SP, Brazil
dc.format.extent1049-1055
dc.identifierhttp://dx.doi.org/10.1016/j.physa.2007.10.044
dc.identifier.citationPhysica A-statistical Mechanics and Its Applications. Amsterdam: Elsevier B.V., v. 387, n. 4, p. 1049-1055, 2008.
dc.identifier.doi10.1016/j.physa.2007.10.044
dc.identifier.issn0378-4371
dc.identifier.lattes7977035910952141
dc.identifier.urihttp://hdl.handle.net/11449/17661
dc.identifier.wosWOS:000252613300029
dc.language.isoeng
dc.publisherElsevier B.V.
dc.relation.ispartofPhysica A: Statistical Mechanics and Its Applications
dc.relation.ispartofjcr2.132
dc.relation.ispartofsjr0,773
dc.rights.accessRightsAcesso restrito
dc.sourceWeb of Science
dc.subjectbiological networksen
dc.subjectcomplex systemsen
dc.subjectgene essentialityen
dc.subjectmachine learningen
dc.titleIn silico network topology-based prediction of gene essentialityen
dc.typeArtigo
dcterms.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dcterms.rightsHolderElsevier B.V.
unesp.author.lattes7977035910952141
unesp.author.orcid0000-0001-7463-4303[6]
unesp.author.orcid0000-0002-8278-240X[2]
unesp.author.orcid0000-0003-2449-5477[4]
unesp.campusUniversidade Estadual Paulista (Unesp), Instituto de Biociências, Botucatupt

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Artigos - Física e Biofísica - IBB