LDA vs QDA for FT-MIR prostate cancer tissue classification

Detalhes bibliográficos
Autor(a) principal: Araújo, Aurigena Antunes de
Data de Publicação: 2017
Outros Autores: Siqueira, Laurinda F.S., Araújo Júnior, Raimundo F., Morais, Camilo L.M., Lima, Kássio M.G.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFRN
Texto Completo: https://repositorio.ufrn.br/handle/123456789/49621
https://doi.org/10.1016/j.chemolab.2017.01.021
Resumo: Discrimination/classification of biological material a ta molecular level is one of the key aims of chemometrics applied to biospectroscopic data. Two discriminant functions, namely Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA), were considered in this study for prostate cancer classification based on FT-MIR data, and illustrated graphically as boundary methods. Principal Component Analysis (PCA) was applied as a variable/dimensionality reduction method and Genetic Algorithm (GA) as variable selection method, followed by LDA and QDA. The performance of each method was determined using 40–100 MIR spectra per tissue sample (n=45), previously classified according to Gleason traditional grading by pathologists. The methods were used to separate the two-category of prostate cancer: Low grade (Gleason grade 2) vs. High grade (Gleason grade 3 and 4). The models were optimized using a training set and their performance was evaluated using a test set. Classification rates and quality metrics (Sensitivity, Specificity, Positive (or Precision) and Negative Predictive Values, Youden's index, and Positive and Negative Likelihood Ratios) were computed for each model. QDA-based models obtained higher classification rates and quality performance than LDA-based models. The models studied identify that secondary protein structure variations and DNA/RNA alterations are the main biomolecular ‘difference markers’ for prostate cancer grades.
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spelling Araújo, Aurigena Antunes deSiqueira, Laurinda F.S.Araújo Júnior, Raimundo F.Morais, Camilo L.M.Lima, Kássio M.G.2022-10-24T21:42:59Z2022-10-24T21:42:59Z2017-03-15ARAÚJO, Aurigena Antunes de et al.LDA vs QDA for FT-MIR prostate cancer tissue classification. Chemometrics and Intelligent Laboratory Systems (Print), v. 162, p. 123-129, 2017. Disponível em: <https://www.sciencedirect.com/science/article/pii/S0169743916303318?via%3Dihub>. Acesso em: 21 mar. 2018.0169-7439https://repositorio.ufrn.br/handle/123456789/49621https://doi.org/10.1016/j.chemolab.2017.01.021ElsevierFT-MIRLDAQDATissueProstate cancerLDA vs QDA for FT-MIR prostate cancer tissue classificationinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleDiscrimination/classification of biological material a ta molecular level is one of the key aims of chemometrics applied to biospectroscopic data. Two discriminant functions, namely Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA), were considered in this study for prostate cancer classification based on FT-MIR data, and illustrated graphically as boundary methods. Principal Component Analysis (PCA) was applied as a variable/dimensionality reduction method and Genetic Algorithm (GA) as variable selection method, followed by LDA and QDA. The performance of each method was determined using 40–100 MIR spectra per tissue sample (n=45), previously classified according to Gleason traditional grading by pathologists. The methods were used to separate the two-category of prostate cancer: Low grade (Gleason grade 2) vs. High grade (Gleason grade 3 and 4). The models were optimized using a training set and their performance was evaluated using a test set. Classification rates and quality metrics (Sensitivity, Specificity, Positive (or Precision) and Negative Predictive Values, Youden's index, and Positive and Negative Likelihood Ratios) were computed for each model. QDA-based models obtained higher classification rates and quality performance than LDA-based models. The models studied identify that secondary protein structure variations and DNA/RNA alterations are the main biomolecular ‘difference markers’ for prostate cancer grades.info:eu-repo/semantics/openAccessengreponame:Repositório Institucional da UFRNinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRNLICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.ufrn.br/bitstream/123456789/49621/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52123456789/496212022-10-24 18:45:47.797oai:https://repositorio.ufrn.br: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Repositório de PublicaçõesPUBhttp://repositorio.ufrn.br/oai/opendoar:2022-10-24T21:45:47Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)false
dc.title.pt_BR.fl_str_mv LDA vs QDA for FT-MIR prostate cancer tissue classification
title LDA vs QDA for FT-MIR prostate cancer tissue classification
spellingShingle LDA vs QDA for FT-MIR prostate cancer tissue classification
Araújo, Aurigena Antunes de
FT-MIR
LDA
QDA
Tissue
Prostate cancer
title_short LDA vs QDA for FT-MIR prostate cancer tissue classification
title_full LDA vs QDA for FT-MIR prostate cancer tissue classification
title_fullStr LDA vs QDA for FT-MIR prostate cancer tissue classification
title_full_unstemmed LDA vs QDA for FT-MIR prostate cancer tissue classification
title_sort LDA vs QDA for FT-MIR prostate cancer tissue classification
author Araújo, Aurigena Antunes de
author_facet Araújo, Aurigena Antunes de
Siqueira, Laurinda F.S.
Araújo Júnior, Raimundo F.
Morais, Camilo L.M.
Lima, Kássio M.G.
author_role author
author2 Siqueira, Laurinda F.S.
Araújo Júnior, Raimundo F.
Morais, Camilo L.M.
Lima, Kássio M.G.
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Araújo, Aurigena Antunes de
Siqueira, Laurinda F.S.
Araújo Júnior, Raimundo F.
Morais, Camilo L.M.
Lima, Kássio M.G.
dc.subject.por.fl_str_mv FT-MIR
LDA
QDA
Tissue
Prostate cancer
topic FT-MIR
LDA
QDA
Tissue
Prostate cancer
description Discrimination/classification of biological material a ta molecular level is one of the key aims of chemometrics applied to biospectroscopic data. Two discriminant functions, namely Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA), were considered in this study for prostate cancer classification based on FT-MIR data, and illustrated graphically as boundary methods. Principal Component Analysis (PCA) was applied as a variable/dimensionality reduction method and Genetic Algorithm (GA) as variable selection method, followed by LDA and QDA. The performance of each method was determined using 40–100 MIR spectra per tissue sample (n=45), previously classified according to Gleason traditional grading by pathologists. The methods were used to separate the two-category of prostate cancer: Low grade (Gleason grade 2) vs. High grade (Gleason grade 3 and 4). The models were optimized using a training set and their performance was evaluated using a test set. Classification rates and quality metrics (Sensitivity, Specificity, Positive (or Precision) and Negative Predictive Values, Youden's index, and Positive and Negative Likelihood Ratios) were computed for each model. QDA-based models obtained higher classification rates and quality performance than LDA-based models. The models studied identify that secondary protein structure variations and DNA/RNA alterations are the main biomolecular ‘difference markers’ for prostate cancer grades.
publishDate 2017
dc.date.issued.fl_str_mv 2017-03-15
dc.date.accessioned.fl_str_mv 2022-10-24T21:42:59Z
dc.date.available.fl_str_mv 2022-10-24T21:42:59Z
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.citation.fl_str_mv ARAÚJO, Aurigena Antunes de et al.LDA vs QDA for FT-MIR prostate cancer tissue classification. Chemometrics and Intelligent Laboratory Systems (Print), v. 162, p. 123-129, 2017. Disponível em: <https://www.sciencedirect.com/science/article/pii/S0169743916303318?via%3Dihub>. Acesso em: 21 mar. 2018.
dc.identifier.uri.fl_str_mv https://repositorio.ufrn.br/handle/123456789/49621
dc.identifier.issn.none.fl_str_mv 0169-7439
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1016/j.chemolab.2017.01.021
identifier_str_mv ARAÚJO, Aurigena Antunes de et al.LDA vs QDA for FT-MIR prostate cancer tissue classification. Chemometrics and Intelligent Laboratory Systems (Print), v. 162, p. 123-129, 2017. Disponível em: <https://www.sciencedirect.com/science/article/pii/S0169743916303318?via%3Dihub>. Acesso em: 21 mar. 2018.
0169-7439
url https://repositorio.ufrn.br/handle/123456789/49621
https://doi.org/10.1016/j.chemolab.2017.01.021
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language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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instname:Universidade Federal do Rio Grande do Norte (UFRN)
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