An integrated approach to identify bimodal genes associated with prognosis in cancer

Detalhes bibliográficos
Autor(a) principal: Justino, Josivan Ribeiro
Data de Publicação: 2021
Outros Autores: Reis, Clovis Ferreira dos, Fonseca, André Luiz, Souza, Sandro José de, Stransky, Beatriz
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFRN
Texto Completo: https://repositorio.ufrn.br/handle/123456789/47071
Resumo: Bimodal gene expression (where a gene expression distribution has two maxima) is associated with phenotypic diversity in different biological systems. A critical issue, thus, is the integration of expression and phenotype data to identify genuine associations. Here, we developed tools that allow both: i) the identification of genes with bimodal gene expression and ii) their association with prognosis in cancer patients from The Cancer Genome Atlas (TCGA). Bimodality was observed for 554 genes in expression data from 25 tumor types. Furthermore, 96 of these genes presented different prognosis when patients belonging to the two expression peaks were compared. The software to execute the method and the corresponding documentation are available at the Data access section
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spelling Justino, Josivan RibeiroReis, Clovis Ferreira dosFonseca, André LuizSouza, Sandro José deStransky, Beatriz2022-04-29T16:34:17Z2022-04-29T16:34:17Z2021-07-08JUSTINO, Josivan Ribeiro; REIS, Clovis Ferreira dos; FONSECA, André Luiz; SOUZA, Sandro José de; STRANSKY, Beatriz. An integrated approach to identify bimodal genes associated with prognosis in cancer. Genetics and Molecular Biology, Ribeirão Preto, v. 44, n.3, p.1-8, 2021. Disponível em: https://www.scielo.br/j/gmb/a/cPtFJjpHJf5yWTz7bVWdyvx/?lang=en. Acesso em: 29 abr. 2022.https://repositorio.ufrn.br/handle/123456789/4707110.1590/1678-4685-GMB-2021-0109FapUNIFESP (SciELO)Attribution 3.0 Brazilhttp://creativecommons.org/licenses/by/3.0/br/info:eu-repo/semantics/openAccessCancerGene expressionsBimodal distributionGaussian Mixture ModelSurvival analysisAn integrated approach to identify bimodal genes associated with prognosis in cancerinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleBimodal gene expression (where a gene expression distribution has two maxima) is associated with phenotypic diversity in different biological systems. A critical issue, thus, is the integration of expression and phenotype data to identify genuine associations. Here, we developed tools that allow both: i) the identification of genes with bimodal gene expression and ii) their association with prognosis in cancer patients from The Cancer Genome Atlas (TCGA). Bimodality was observed for 554 genes in expression data from 25 tumor types. Furthermore, 96 of these genes presented different prognosis when patients belonging to the two expression peaks were compared. The software to execute the method and the corresponding documentation are available at the Data access sectionengreponame:Repositório Institucional da UFRNinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRNORIGINALAnIntegratedApproach_Souza_2021.pdfAnIntegratedApproach_Souza_2021.pdfAnIntegratedApproach_Souza_2021application/pdf3817479https://repositorio.ufrn.br/bitstream/123456789/47071/1/AnIntegratedApproach_Souza_2021.pdf75c1abb24f958ca1f5049a87897f0d94MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8914https://repositorio.ufrn.br/bitstream/123456789/47071/2/license_rdf4d2950bda3d176f570a9f8b328dfbbefMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81484https://repositorio.ufrn.br/bitstream/123456789/47071/3/license.txte9597aa2854d128fd968be5edc8a28d9MD53123456789/470712022-04-29 13:34:18.099oai:https://repositorio.ufrn.br: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Repositório de PublicaçõesPUBhttp://repositorio.ufrn.br/oai/opendoar:2022-04-29T16:34:18Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)false
dc.title.pt_BR.fl_str_mv An integrated approach to identify bimodal genes associated with prognosis in cancer
title An integrated approach to identify bimodal genes associated with prognosis in cancer
spellingShingle An integrated approach to identify bimodal genes associated with prognosis in cancer
Justino, Josivan Ribeiro
Cancer
Gene expressions
Bimodal distribution
Gaussian Mixture Model
Survival analysis
title_short An integrated approach to identify bimodal genes associated with prognosis in cancer
title_full An integrated approach to identify bimodal genes associated with prognosis in cancer
title_fullStr An integrated approach to identify bimodal genes associated with prognosis in cancer
title_full_unstemmed An integrated approach to identify bimodal genes associated with prognosis in cancer
title_sort An integrated approach to identify bimodal genes associated with prognosis in cancer
author Justino, Josivan Ribeiro
author_facet Justino, Josivan Ribeiro
Reis, Clovis Ferreira dos
Fonseca, André Luiz
Souza, Sandro José de
Stransky, Beatriz
author_role author
author2 Reis, Clovis Ferreira dos
Fonseca, André Luiz
Souza, Sandro José de
Stransky, Beatriz
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Justino, Josivan Ribeiro
Reis, Clovis Ferreira dos
Fonseca, André Luiz
Souza, Sandro José de
Stransky, Beatriz
dc.subject.por.fl_str_mv Cancer
Gene expressions
Bimodal distribution
Gaussian Mixture Model
Survival analysis
topic Cancer
Gene expressions
Bimodal distribution
Gaussian Mixture Model
Survival analysis
description Bimodal gene expression (where a gene expression distribution has two maxima) is associated with phenotypic diversity in different biological systems. A critical issue, thus, is the integration of expression and phenotype data to identify genuine associations. Here, we developed tools that allow both: i) the identification of genes with bimodal gene expression and ii) their association with prognosis in cancer patients from The Cancer Genome Atlas (TCGA). Bimodality was observed for 554 genes in expression data from 25 tumor types. Furthermore, 96 of these genes presented different prognosis when patients belonging to the two expression peaks were compared. The software to execute the method and the corresponding documentation are available at the Data access section
publishDate 2021
dc.date.issued.fl_str_mv 2021-07-08
dc.date.accessioned.fl_str_mv 2022-04-29T16:34:17Z
dc.date.available.fl_str_mv 2022-04-29T16:34:17Z
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.citation.fl_str_mv JUSTINO, Josivan Ribeiro; REIS, Clovis Ferreira dos; FONSECA, André Luiz; SOUZA, Sandro José de; STRANSKY, Beatriz. An integrated approach to identify bimodal genes associated with prognosis in cancer. Genetics and Molecular Biology, Ribeirão Preto, v. 44, n.3, p.1-8, 2021. Disponível em: https://www.scielo.br/j/gmb/a/cPtFJjpHJf5yWTz7bVWdyvx/?lang=en. Acesso em: 29 abr. 2022.
dc.identifier.uri.fl_str_mv https://repositorio.ufrn.br/handle/123456789/47071
dc.identifier.doi.none.fl_str_mv 10.1590/1678-4685-GMB-2021-0109
identifier_str_mv JUSTINO, Josivan Ribeiro; REIS, Clovis Ferreira dos; FONSECA, André Luiz; SOUZA, Sandro José de; STRANSKY, Beatriz. An integrated approach to identify bimodal genes associated with prognosis in cancer. Genetics and Molecular Biology, Ribeirão Preto, v. 44, n.3, p.1-8, 2021. Disponível em: https://www.scielo.br/j/gmb/a/cPtFJjpHJf5yWTz7bVWdyvx/?lang=en. Acesso em: 29 abr. 2022.
10.1590/1678-4685-GMB-2021-0109
url https://repositorio.ufrn.br/handle/123456789/47071
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv Attribution 3.0 Brazil
http://creativecommons.org/licenses/by/3.0/br/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 3.0 Brazil
http://creativecommons.org/licenses/by/3.0/br/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv FapUNIFESP (SciELO)
publisher.none.fl_str_mv FapUNIFESP (SciELO)
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFRN
instname:Universidade Federal do Rio Grande do Norte (UFRN)
instacron:UFRN
instname_str Universidade Federal do Rio Grande do Norte (UFRN)
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collection Repositório Institucional da UFRN
bitstream.url.fl_str_mv https://repositorio.ufrn.br/bitstream/123456789/47071/1/AnIntegratedApproach_Souza_2021.pdf
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