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Performance of PLS regression coefficients in selecting variables for each response of a multivariate PLS for omics-type data
Multivariate partial least square (PLS) regression allows the modeling of complex biological events, by considering different factors at the same time. It is unaffected by data collinearity, representing a valuable method for modeling high-dimensional biological data (as derived from genomics, prote...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove Medical Press
2009
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3169946/ https://www.ncbi.nlm.nih.gov/pubmed/21918616 |
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author | Palermo, Giuseppe Piraino, Paolo Zucht, Hans-Dieter |
author_facet | Palermo, Giuseppe Piraino, Paolo Zucht, Hans-Dieter |
author_sort | Palermo, Giuseppe |
collection | PubMed |
description | Multivariate partial least square (PLS) regression allows the modeling of complex biological events, by considering different factors at the same time. It is unaffected by data collinearity, representing a valuable method for modeling high-dimensional biological data (as derived from genomics, proteomics and peptidomics). In presence of multiple responses, it is of particular interest how to appropriately “dissect” the model, to reveal the importance of single attributes with regard to individual responses (for example, variable selection). In this paper, performances of multivariate PLS regression coefficients, in selecting relevant predictors for different responses in omics-type of data, were investigated by means of a receiver operating characteristic (ROC) analysis. For this purpose, simulated data, mimicking the covariance structures of microarray and liquid chromatography mass spectrometric data, were used to generate matrices of predictors and responses. The relevant predictors were set a priori. The influences of noise, the source of data with different covariance structure and the size of relevant predictors were investigated. Results demonstrate the applicability of PLS regression coefficients in selecting variables for each response of a multivariate PLS, in omics-type of data. Comparisons with other feature selection methods, such as variable importance in the projection scores, principal component regression, and least absolute shrinkage and selection operator regression were also provided. |
format | Online Article Text |
id | pubmed-3169946 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | Dove Medical Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-31699462011-09-14 Performance of PLS regression coefficients in selecting variables for each response of a multivariate PLS for omics-type data Palermo, Giuseppe Piraino, Paolo Zucht, Hans-Dieter Adv Appl Bioinforma Chem Original Research Multivariate partial least square (PLS) regression allows the modeling of complex biological events, by considering different factors at the same time. It is unaffected by data collinearity, representing a valuable method for modeling high-dimensional biological data (as derived from genomics, proteomics and peptidomics). In presence of multiple responses, it is of particular interest how to appropriately “dissect” the model, to reveal the importance of single attributes with regard to individual responses (for example, variable selection). In this paper, performances of multivariate PLS regression coefficients, in selecting relevant predictors for different responses in omics-type of data, were investigated by means of a receiver operating characteristic (ROC) analysis. For this purpose, simulated data, mimicking the covariance structures of microarray and liquid chromatography mass spectrometric data, were used to generate matrices of predictors and responses. The relevant predictors were set a priori. The influences of noise, the source of data with different covariance structure and the size of relevant predictors were investigated. Results demonstrate the applicability of PLS regression coefficients in selecting variables for each response of a multivariate PLS, in omics-type of data. Comparisons with other feature selection methods, such as variable importance in the projection scores, principal component regression, and least absolute shrinkage and selection operator regression were also provided. Dove Medical Press 2009-05-13 /pmc/articles/PMC3169946/ /pubmed/21918616 Text en © 2009 Palermo et al, publisher and licensee Dove Medical Press Ltd. This is an Open Access article which permits unrestricted noncommercial use, provided the original work is properly cited. |
spellingShingle | Original Research Palermo, Giuseppe Piraino, Paolo Zucht, Hans-Dieter Performance of PLS regression coefficients in selecting variables for each response of a multivariate PLS for omics-type data |
title | Performance of PLS regression coefficients in selecting variables for each response of a multivariate PLS for omics-type data |
title_full | Performance of PLS regression coefficients in selecting variables for each response of a multivariate PLS for omics-type data |
title_fullStr | Performance of PLS regression coefficients in selecting variables for each response of a multivariate PLS for omics-type data |
title_full_unstemmed | Performance of PLS regression coefficients in selecting variables for each response of a multivariate PLS for omics-type data |
title_short | Performance of PLS regression coefficients in selecting variables for each response of a multivariate PLS for omics-type data |
title_sort | performance of pls regression coefficients in selecting variables for each response of a multivariate pls for omics-type data |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3169946/ https://www.ncbi.nlm.nih.gov/pubmed/21918616 |
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