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Supervised Wavelet Method to Predict Patient Survival from Gene Expression Data
In microarray studies, the number of samples is relatively small compared to the number of genes per sample. An important aspect of microarray studies is the prediction of patient survival based on their gene expression profile. This naturally calls for the use of a dimension reduction procedure tog...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4235600/ https://www.ncbi.nlm.nih.gov/pubmed/25538955 http://dx.doi.org/10.1155/2014/618412 |
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author | Farhadian, Maryam Lisboa, Paulo J. G. Moghimbeigi, Abbas Poorolajal, Jalal Mahjub, Hossein |
author_facet | Farhadian, Maryam Lisboa, Paulo J. G. Moghimbeigi, Abbas Poorolajal, Jalal Mahjub, Hossein |
author_sort | Farhadian, Maryam |
collection | PubMed |
description | In microarray studies, the number of samples is relatively small compared to the number of genes per sample. An important aspect of microarray studies is the prediction of patient survival based on their gene expression profile. This naturally calls for the use of a dimension reduction procedure together with the survival prediction model. In this study, a new method based on combining wavelet approximation coefficients and Cox regression was presented. The proposed method was compared with supervised principal component and supervised partial least squares methods. The different fitted Cox models based on supervised wavelet approximation coefficients, the top number of supervised principal components, and partial least squares components were applied to the data. The results showed that the prediction performance of the Cox model based on supervised wavelet feature extraction was superior to the supervised principal components and partial least squares components. The results suggested the possibility of developing new tools based on wavelets for the dimensionally reduction of microarray data sets in the context of survival analysis. |
format | Online Article Text |
id | pubmed-4235600 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-42356002014-12-23 Supervised Wavelet Method to Predict Patient Survival from Gene Expression Data Farhadian, Maryam Lisboa, Paulo J. G. Moghimbeigi, Abbas Poorolajal, Jalal Mahjub, Hossein ScientificWorldJournal Research Article In microarray studies, the number of samples is relatively small compared to the number of genes per sample. An important aspect of microarray studies is the prediction of patient survival based on their gene expression profile. This naturally calls for the use of a dimension reduction procedure together with the survival prediction model. In this study, a new method based on combining wavelet approximation coefficients and Cox regression was presented. The proposed method was compared with supervised principal component and supervised partial least squares methods. The different fitted Cox models based on supervised wavelet approximation coefficients, the top number of supervised principal components, and partial least squares components were applied to the data. The results showed that the prediction performance of the Cox model based on supervised wavelet feature extraction was superior to the supervised principal components and partial least squares components. The results suggested the possibility of developing new tools based on wavelets for the dimensionally reduction of microarray data sets in the context of survival analysis. Hindawi Publishing Corporation 2014 2014-11-03 /pmc/articles/PMC4235600/ /pubmed/25538955 http://dx.doi.org/10.1155/2014/618412 Text en Copyright © 2014 Maryam Farhadian et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Farhadian, Maryam Lisboa, Paulo J. G. Moghimbeigi, Abbas Poorolajal, Jalal Mahjub, Hossein Supervised Wavelet Method to Predict Patient Survival from Gene Expression Data |
title | Supervised Wavelet Method to Predict Patient Survival from Gene Expression Data |
title_full | Supervised Wavelet Method to Predict Patient Survival from Gene Expression Data |
title_fullStr | Supervised Wavelet Method to Predict Patient Survival from Gene Expression Data |
title_full_unstemmed | Supervised Wavelet Method to Predict Patient Survival from Gene Expression Data |
title_short | Supervised Wavelet Method to Predict Patient Survival from Gene Expression Data |
title_sort | supervised wavelet method to predict patient survival from gene expression data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4235600/ https://www.ncbi.nlm.nih.gov/pubmed/25538955 http://dx.doi.org/10.1155/2014/618412 |
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