Application of a Computational Method for the Early Diagnosis of Prostate Cancer Using Proteomic Pattern Recognition
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://doi.org/10.34627/rcc.v11i0.85 |
Resumo: | This paper presents a method based on the recognition of proteomic patterns for the early diagnosis of prostate cancer, using computational techniques, applied in the database of SELDI-TOF proteomic patterns. The method is based on classifying the individual as to the portability stage of prostate cancer. To do so, the Independent Component Analysis (ICA) technique is used to extract the characteristics, after which are utilized the algorithm of Maximum Relevance and Minimum Redundancy to reduce the computational cost, and finally the Support Vector Machine to obtain the classification. The best result of the method was obtained with a vector of 27 characteristics, achieving accuracy, specificity and sensitivity, respectively of 89.21%, 83.68% and 95.08%. |
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oai:ojs2.journals.uab.pt:article/85 |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Application of a Computational Method for the Early Diagnosis of Prostate Cancer Using Proteomic Pattern RecognitionAplicação de um Método Computacional para o Diagnóstico Precoce do Câncer de Próstata Usando Reconhecimento de Padrões ProteômicosThis paper presents a method based on the recognition of proteomic patterns for the early diagnosis of prostate cancer, using computational techniques, applied in the database of SELDI-TOF proteomic patterns. The method is based on classifying the individual as to the portability stage of prostate cancer. To do so, the Independent Component Analysis (ICA) technique is used to extract the characteristics, after which are utilized the algorithm of Maximum Relevance and Minimum Redundancy to reduce the computational cost, and finally the Support Vector Machine to obtain the classification. The best result of the method was obtained with a vector of 27 characteristics, achieving accuracy, specificity and sensitivity, respectively of 89.21%, 83.68% and 95.08%.O presente trabalho apresenta um método baseado em reconhecimento de padrões proteômicos para o diagnóstico precoce do câncer de próstata, utilizando técnicas computacionais, aplicadas na base de dados de padrões proteômicos SELDI-TOF. O método baseia-se em classificar o indivíduo quanto ao estágio de portabilidade do câncer de próstata. Para tanto, usa-se a técnica de Análise de Componentes Independente (ICA) para extrair as características, depois o algoritmo de Máxima Relevância e Mínima Redundância para reduzir o custo computacional, e finalmente a Máquina de Vetores de Suporte para a classificação. Obtêm-se o melhor resultado do método com um vetor de 27 características, alcançando acurácia, especificidade e sensibilidade, respectivamente de 89,21%, 83,68% e 95,08%.Universidade Aberta2018-04-13info:eu-repo/semantics/articleinfo:eu-repo/semantics/otherinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://doi.org/10.34627/rcc.v11i0.85oai:ojs2.journals.uab.pt:article/85Revista de Ciências da Computação; v. 11 (2016); 1-142182-18011646-633010.34627/rcc.v11i0reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAPporhttps://journals.uab.pt/index.php/rcc/article/view/85https://doi.org/10.34627/rcc.v11i0.85https://journals.uab.pt/index.php/rcc/article/view/85/50Direitos de Autor (c) 2018 Universidade Abertahttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessMontes, ElzenirCampos, LúcioAraujo, WesleySantana, EwaldoAraujo, Claudyane2022-10-25T11:31:54Zoai:ojs2.journals.uab.pt:article/85Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:14:00.015010Repositório 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 |
Application of a Computational Method for the Early Diagnosis of Prostate Cancer Using Proteomic Pattern Recognition Aplicação de um Método Computacional para o Diagnóstico Precoce do Câncer de Próstata Usando Reconhecimento de Padrões Proteômicos |
title |
Application of a Computational Method for the Early Diagnosis of Prostate Cancer Using Proteomic Pattern Recognition |
spellingShingle |
Application of a Computational Method for the Early Diagnosis of Prostate Cancer Using Proteomic Pattern Recognition Montes, Elzenir |
title_short |
Application of a Computational Method for the Early Diagnosis of Prostate Cancer Using Proteomic Pattern Recognition |
title_full |
Application of a Computational Method for the Early Diagnosis of Prostate Cancer Using Proteomic Pattern Recognition |
title_fullStr |
Application of a Computational Method for the Early Diagnosis of Prostate Cancer Using Proteomic Pattern Recognition |
title_full_unstemmed |
Application of a Computational Method for the Early Diagnosis of Prostate Cancer Using Proteomic Pattern Recognition |
title_sort |
Application of a Computational Method for the Early Diagnosis of Prostate Cancer Using Proteomic Pattern Recognition |
author |
Montes, Elzenir |
author_facet |
Montes, Elzenir Campos, Lúcio Araujo, Wesley Santana, Ewaldo Araujo, Claudyane |
author_role |
author |
author2 |
Campos, Lúcio Araujo, Wesley Santana, Ewaldo Araujo, Claudyane |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Montes, Elzenir Campos, Lúcio Araujo, Wesley Santana, Ewaldo Araujo, Claudyane |
description |
This paper presents a method based on the recognition of proteomic patterns for the early diagnosis of prostate cancer, using computational techniques, applied in the database of SELDI-TOF proteomic patterns. The method is based on classifying the individual as to the portability stage of prostate cancer. To do so, the Independent Component Analysis (ICA) technique is used to extract the characteristics, after which are utilized the algorithm of Maximum Relevance and Minimum Redundancy to reduce the computational cost, and finally the Support Vector Machine to obtain the classification. The best result of the method was obtained with a vector of 27 characteristics, achieving accuracy, specificity and sensitivity, respectively of 89.21%, 83.68% and 95.08%. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-04-13 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/other |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://doi.org/10.34627/rcc.v11i0.85 oai:ojs2.journals.uab.pt:article/85 |
url |
https://doi.org/10.34627/rcc.v11i0.85 |
identifier_str_mv |
oai:ojs2.journals.uab.pt:article/85 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://journals.uab.pt/index.php/rcc/article/view/85 https://doi.org/10.34627/rcc.v11i0.85 https://journals.uab.pt/index.php/rcc/article/view/85/50 |
dc.rights.driver.fl_str_mv |
Direitos de Autor (c) 2018 Universidade Aberta http://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Direitos de Autor (c) 2018 Universidade Aberta http://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Aberta |
publisher.none.fl_str_mv |
Universidade Aberta |
dc.source.none.fl_str_mv |
Revista de Ciências da Computação; v. 11 (2016); 1-14 2182-1801 1646-6330 10.34627/rcc.v11i0 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 |
institution |
RCAAP |
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 |
repository.mail.fl_str_mv |
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1799130592951926784 |