Investigation of the use of functional interaction networks and enriched functional interaction networks in methods for identifying significant mutations applied to cancer research

Detalhes bibliográficos
Ano de defesa: 2025
Autor(a) principal: Souza, Alfredo Guilherme da Silva
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Tese
Tipo de acesso: Acesso aberto
Idioma: eng
Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Biological networks
Cancer
Câncer
Driver mutations
Mutações driver
Redes biológicas
Link de acesso: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-02092025-104234/
Resumo: The cell is the basic unit of life and carries a copy of the genetic material (DNA). DNA determines various human characteristics, as well as biological phenomena within the organism. It consists of a vast amount of information, encoded in nucleotide sequences. Due to the large volume of data, numerous representation techniques can be employed, such as the use of complex networks, which enable not only the representation of DNA but also relationships among genes, proteins, genes that share the same biological activities (pathways), and other biological information. In the literature, numerous computational methods have been proposed that, by manipulating patient mutation data associated with functional interaction networks, can identify potential genes linked to a specific type of cancer under study. Various methods employ different network structures, incorporating distinct pathwayseither complete or partially generated through specific processes associated with the databases used to construct the network. This study investigates the impact of using enriched biological networks in the identification of driver genes. The analysis seeks to determine whether enriching the network with prioritized genes for each cancer type improves the accuracy of driver gene identification methods compared to using conventional biological networks. Furthermore, the study evaluates whether the inclusion of this prioritization process positively influences the detection of driver genes, highlighting functionally more relevant genes for the cancer type under investigation.
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spelling Investigation of the use of functional interaction networks and enriched functional interaction networks in methods for identifying significant mutations applied to cancer researchInvestigação do uso de redes de interação funcional e redes de interação funcional enriquecidas em métodos para identificar mutações significativas aplicadas à pesquisa do câncerBiological networksCancerCâncerDriver mutationsMutações driverRedes biológicasThe cell is the basic unit of life and carries a copy of the genetic material (DNA). DNA determines various human characteristics, as well as biological phenomena within the organism. It consists of a vast amount of information, encoded in nucleotide sequences. Due to the large volume of data, numerous representation techniques can be employed, such as the use of complex networks, which enable not only the representation of DNA but also relationships among genes, proteins, genes that share the same biological activities (pathways), and other biological information. In the literature, numerous computational methods have been proposed that, by manipulating patient mutation data associated with functional interaction networks, can identify potential genes linked to a specific type of cancer under study. Various methods employ different network structures, incorporating distinct pathwayseither complete or partially generated through specific processes associated with the databases used to construct the network. This study investigates the impact of using enriched biological networks in the identification of driver genes. The analysis seeks to determine whether enriching the network with prioritized genes for each cancer type improves the accuracy of driver gene identification methods compared to using conventional biological networks. Furthermore, the study evaluates whether the inclusion of this prioritization process positively influences the detection of driver genes, highlighting functionally more relevant genes for the cancer type under investigation.A célula é a unidade básica da vida e carrega uma cópia do material genético (DNA). O DNA determina diversas características humanas, bem como fenômenos biológicos dentro do organismo. Ele consiste em uma vasta quantidade de informação, codificada em sequências de nucleotídeos. Devido ao grande volume de dados, diversas técnicas de representação podem ser empregadas, como o uso de redes complexas, que permitem não apenas a representação do DNA, mas também as relações entre genes, proteínas, genes que compartilham as mesmas atividades biológicas (vias metabólicas) e outras informações biológicas. Na literatura, diversos métodos computacionais foram propostos que, ao manipular dados de mutações de pacientes associados a redes de interação funcional, podem identificar genes potenciais ligados a um tipo específico de câncer em estudo. Vários métodos empregam diferentes estruturas de rede, incorporando vias distintascompletas ou parcialmente geradas por meio de processos específicos associados aos bancos de dados utilizados para construir a rede. Este estudo investiga o impacto do uso de redes biológicas enriquecidas na identificação de genes drivers. A análise busca determinar se o enriquecimento da rede com genes priorizados para cada tipo de câncer melhora a precisão dos métodos de identificação de genes drivers, em comparação com o uso de redes biológicas convencionais. Além disso, o estudo avalia se a inclusão desse processo de priorização influencia positivamente a detecção de genes drivers, destacando genes funcionalmente mais relevantes para o tipo de câncer em investigação.Biblioteca Digitais de Teses e Dissertações da USPSimão, Adenilso da SilvaSouza, Alfredo Guilherme da Silva2025-06-27info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdfhttps://www.teses.usp.br/teses/disponiveis/55/55134/tde-02092025-104234/reponame:Biblioteca Digital de Teses e Dissertações da USPinstname:Universidade de São Paulo (USP)instacron:USPLiberar o conteúdo para acesso público.info:eu-repo/semantics/openAccesseng2025-09-02T14:35:03Zoai:teses.usp.br:tde-02092025-104234Biblioteca Digital de Teses e Dissertaçõeshttp://www.teses.usp.br/PUBhttp://www.teses.usp.br/cgi-bin/mtd2br.plvirginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.bropendoar:27212025-09-02T14:35:03Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP)false
dc.title.none.fl_str_mv Investigation of the use of functional interaction networks and enriched functional interaction networks in methods for identifying significant mutations applied to cancer research
Investigação do uso de redes de interação funcional e redes de interação funcional enriquecidas em métodos para identificar mutações significativas aplicadas à pesquisa do câncer
title Investigation of the use of functional interaction networks and enriched functional interaction networks in methods for identifying significant mutations applied to cancer research
spellingShingle Investigation of the use of functional interaction networks and enriched functional interaction networks in methods for identifying significant mutations applied to cancer research
Souza, Alfredo Guilherme da Silva
Biological networks
Cancer
Câncer
Driver mutations
Mutações driver
Redes biológicas
title_short Investigation of the use of functional interaction networks and enriched functional interaction networks in methods for identifying significant mutations applied to cancer research
title_full Investigation of the use of functional interaction networks and enriched functional interaction networks in methods for identifying significant mutations applied to cancer research
title_fullStr Investigation of the use of functional interaction networks and enriched functional interaction networks in methods for identifying significant mutations applied to cancer research
title_full_unstemmed Investigation of the use of functional interaction networks and enriched functional interaction networks in methods for identifying significant mutations applied to cancer research
title_sort Investigation of the use of functional interaction networks and enriched functional interaction networks in methods for identifying significant mutations applied to cancer research
author Souza, Alfredo Guilherme da Silva
author_facet Souza, Alfredo Guilherme da Silva
author_role author
dc.contributor.none.fl_str_mv Simão, Adenilso da Silva
dc.contributor.author.fl_str_mv Souza, Alfredo Guilherme da Silva
dc.subject.por.fl_str_mv Biological networks
Cancer
Câncer
Driver mutations
Mutações driver
Redes biológicas
topic Biological networks
Cancer
Câncer
Driver mutations
Mutações driver
Redes biológicas
description The cell is the basic unit of life and carries a copy of the genetic material (DNA). DNA determines various human characteristics, as well as biological phenomena within the organism. It consists of a vast amount of information, encoded in nucleotide sequences. Due to the large volume of data, numerous representation techniques can be employed, such as the use of complex networks, which enable not only the representation of DNA but also relationships among genes, proteins, genes that share the same biological activities (pathways), and other biological information. In the literature, numerous computational methods have been proposed that, by manipulating patient mutation data associated with functional interaction networks, can identify potential genes linked to a specific type of cancer under study. Various methods employ different network structures, incorporating distinct pathwayseither complete or partially generated through specific processes associated with the databases used to construct the network. This study investigates the impact of using enriched biological networks in the identification of driver genes. The analysis seeks to determine whether enriching the network with prioritized genes for each cancer type improves the accuracy of driver gene identification methods compared to using conventional biological networks. Furthermore, the study evaluates whether the inclusion of this prioritization process positively influences the detection of driver genes, highlighting functionally more relevant genes for the cancer type under investigation.
publishDate 2025
dc.date.none.fl_str_mv 2025-06-27
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/doctoralThesis
format doctoralThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://www.teses.usp.br/teses/disponiveis/55/55134/tde-02092025-104234/
url https://www.teses.usp.br/teses/disponiveis/55/55134/tde-02092025-104234/
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv
dc.rights.driver.fl_str_mv Liberar o conteúdo para acesso público.
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Liberar o conteúdo para acesso público.
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.coverage.none.fl_str_mv
dc.publisher.none.fl_str_mv Biblioteca Digitais de Teses e Dissertações da USP
publisher.none.fl_str_mv Biblioteca Digitais de Teses e Dissertações da USP
dc.source.none.fl_str_mv
reponame:Biblioteca Digital de Teses e Dissertações da USP
instname:Universidade de São Paulo (USP)
instacron:USP
instname_str Universidade de São Paulo (USP)
instacron_str USP
institution USP
reponame_str Biblioteca Digital de Teses e Dissertações da USP
collection Biblioteca Digital de Teses e Dissertações da USP
repository.name.fl_str_mv Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP)
repository.mail.fl_str_mv virginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.br
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