Classification and biomarker selection in lower-grade glioma using robust sparse logistic regression applied to RNA-seq data

Detalhes bibliográficos
Autor(a) principal: Carrilho, João F.
Data de Publicação: 2022
Outros Autores: Lopes, Marta B.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/149155
Resumo: Funding Information: This work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with references CEECINST/00102/2018 (NOVA MATH, Center for Mathematics and Applications).The results presented are based upon data generated by the TCGA Research Network: https://www.cancer. gov/tcga. Publisher Copyright: © Brazilian Journal of Biometrics.
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spelling Classification and biomarker selection in lower-grade glioma using robust sparse logistic regression applied to RNA-seq dataClassificationElastic net regularizationGliomaRobust StatisticsSparse Logistic regressionEpidemiologyStatistics and ProbabilityAgricultural and Biological Sciences(all)Public Health, Environmental and Occupational HealthApplied MathematicsSDG 3 - Good Health and Well-beingFunding Information: This work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with references CEECINST/00102/2018 (NOVA MATH, Center for Mathematics and Applications).The results presented are based upon data generated by the TCGA Research Network: https://www.cancer. gov/tcga. Publisher Copyright: © Brazilian Journal of Biometrics.Effective diagnosis and treatment in cancer is a barrier for the development of personalized medicine, mostly due to tumor heterogeneity. In the particular case of gliomas, highly heterogeneous brain tumors at the histological, cellular and molecular levels, and exhibiting poor prognosis, the mechanisms behind tumor heterogeneity and progression remain poorly understood. The recent advances in biomedical high-throughput technologies have allowed the generation of large amounts of molecular information from the patients that combined with statistical and machine learning techniques can be used for the definition of glioma subtypes and targeted therapies, an invaluable contribution to disease understanding and effective management. In this work sparse and robust sparse logistic regression models with the elastic net penalty were applied to glioma RNA-seq data from The Cancer Genome Atlas (TCGA), to identify relevant tran-scriptomic features in the separation between lower-grade glioma (LGG) subtypes and identify putative outlying observations. In general, all classification models yielded good accuracies, selecting different sets of genes. Among the genes selected by the models, TXNDC12, TOMM20, PKIA, CARD8 and TAF12 have been reported as genes with relevant role in glioma development and progression. This highlights the suitability of the present approach to disclose relevant genes and fosters the biological validation of non-reported genes.DM - Departamento de MatemáticaNOVALincsCMA - Centro de Matemática e AplicaçõesRUNCarrilho, João F.Lopes, Marta B.2023-02-13T22:19:59Z2022-12-312022-12-31T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article11application/pdfhttp://hdl.handle.net/10362/149155eng1983-0823PURE: 53110590https://doi.org/10.28951/bjb.v40i4.634info: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:RCAAP2024-03-11T05:30:56Zoai:run.unl.pt:10362/149155Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:53:38.024573Repositório 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 Classification and biomarker selection in lower-grade glioma using robust sparse logistic regression applied to RNA-seq data
title Classification and biomarker selection in lower-grade glioma using robust sparse logistic regression applied to RNA-seq data
spellingShingle Classification and biomarker selection in lower-grade glioma using robust sparse logistic regression applied to RNA-seq data
Carrilho, João F.
Classification
Elastic net regularization
Glioma
Robust Statistics
Sparse Logistic regression
Epidemiology
Statistics and Probability
Agricultural and Biological Sciences(all)
Public Health, Environmental and Occupational Health
Applied Mathematics
SDG 3 - Good Health and Well-being
title_short Classification and biomarker selection in lower-grade glioma using robust sparse logistic regression applied to RNA-seq data
title_full Classification and biomarker selection in lower-grade glioma using robust sparse logistic regression applied to RNA-seq data
title_fullStr Classification and biomarker selection in lower-grade glioma using robust sparse logistic regression applied to RNA-seq data
title_full_unstemmed Classification and biomarker selection in lower-grade glioma using robust sparse logistic regression applied to RNA-seq data
title_sort Classification and biomarker selection in lower-grade glioma using robust sparse logistic regression applied to RNA-seq data
author Carrilho, João F.
author_facet Carrilho, João F.
Lopes, Marta B.
author_role author
author2 Lopes, Marta B.
author2_role author
dc.contributor.none.fl_str_mv DM - Departamento de Matemática
NOVALincs
CMA - Centro de Matemática e Aplicações
RUN
dc.contributor.author.fl_str_mv Carrilho, João F.
Lopes, Marta B.
dc.subject.por.fl_str_mv Classification
Elastic net regularization
Glioma
Robust Statistics
Sparse Logistic regression
Epidemiology
Statistics and Probability
Agricultural and Biological Sciences(all)
Public Health, Environmental and Occupational Health
Applied Mathematics
SDG 3 - Good Health and Well-being
topic Classification
Elastic net regularization
Glioma
Robust Statistics
Sparse Logistic regression
Epidemiology
Statistics and Probability
Agricultural and Biological Sciences(all)
Public Health, Environmental and Occupational Health
Applied Mathematics
SDG 3 - Good Health and Well-being
description Funding Information: This work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with references CEECINST/00102/2018 (NOVA MATH, Center for Mathematics and Applications).The results presented are based upon data generated by the TCGA Research Network: https://www.cancer. gov/tcga. Publisher Copyright: © Brazilian Journal of Biometrics.
publishDate 2022
dc.date.none.fl_str_mv 2022-12-31
2022-12-31T00:00:00Z
2023-02-13T22:19:59Z
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/10362/149155
url http://hdl.handle.net/10362/149155
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1983-0823
PURE: 53110590
https://doi.org/10.28951/bjb.v40i4.634
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 11
application/pdf
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
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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
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