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Selecting informative genes for discriminant analysis using multigene expression profiles

BACKGROUND: Gene expression data extracted from microarray experiments have been used to study the difference between mRNA abundance of genes under different conditions. In one of such experiments, thousands of genes are measured simultaneously, which provides a high-dimensional feature space for di...

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Detalles Bibliográficos
Autores principales: Yan, Xin, Zheng, Tian
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2559878/
https://www.ncbi.nlm.nih.gov/pubmed/18831779
http://dx.doi.org/10.1186/1471-2164-9-S2-S14
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author Yan, Xin
Zheng, Tian
author_facet Yan, Xin
Zheng, Tian
author_sort Yan, Xin
collection PubMed
description BACKGROUND: Gene expression data extracted from microarray experiments have been used to study the difference between mRNA abundance of genes under different conditions. In one of such experiments, thousands of genes are measured simultaneously, which provides a high-dimensional feature space for discriminating between different sample classes. However, most of these dimensions are not informative about the between-class difference, and add noises to the discriminant analysis. RESULTS: In this paper we propose and study feature selection methods that evaluate the "informativeness" of a set of genes. Two measures of information based on multigene expression profiles are considered for a backward information-driven screening approach for selecting important gene features. By considering multigene expression profiles, we are able to utilize interaction information among these genes. Using a breast cancer data, we illustrate our methods and compare them to the performance of existing methods. CONCLUSION: We illustrate in this paper that methods considering gene-gene interactions have better classification power in gene expression analysis. In our results, we identify important genes with relative large p-values from single gene tests. This indicates that these are genes with weak marginal information but strong interaction information, which will be overlooked by strategies that only examine individual genes.
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spelling pubmed-25598782008-10-04 Selecting informative genes for discriminant analysis using multigene expression profiles Yan, Xin Zheng, Tian BMC Genomics Research BACKGROUND: Gene expression data extracted from microarray experiments have been used to study the difference between mRNA abundance of genes under different conditions. In one of such experiments, thousands of genes are measured simultaneously, which provides a high-dimensional feature space for discriminating between different sample classes. However, most of these dimensions are not informative about the between-class difference, and add noises to the discriminant analysis. RESULTS: In this paper we propose and study feature selection methods that evaluate the "informativeness" of a set of genes. Two measures of information based on multigene expression profiles are considered for a backward information-driven screening approach for selecting important gene features. By considering multigene expression profiles, we are able to utilize interaction information among these genes. Using a breast cancer data, we illustrate our methods and compare them to the performance of existing methods. CONCLUSION: We illustrate in this paper that methods considering gene-gene interactions have better classification power in gene expression analysis. In our results, we identify important genes with relative large p-values from single gene tests. This indicates that these are genes with weak marginal information but strong interaction information, which will be overlooked by strategies that only examine individual genes. BioMed Central 2008-09-16 /pmc/articles/PMC2559878/ /pubmed/18831779 http://dx.doi.org/10.1186/1471-2164-9-S2-S14 Text en Copyright © 2008 Yan and Zheng; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Yan, Xin
Zheng, Tian
Selecting informative genes for discriminant analysis using multigene expression profiles
title Selecting informative genes for discriminant analysis using multigene expression profiles
title_full Selecting informative genes for discriminant analysis using multigene expression profiles
title_fullStr Selecting informative genes for discriminant analysis using multigene expression profiles
title_full_unstemmed Selecting informative genes for discriminant analysis using multigene expression profiles
title_short Selecting informative genes for discriminant analysis using multigene expression profiles
title_sort selecting informative genes for discriminant analysis using multigene expression profiles
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2559878/
https://www.ncbi.nlm.nih.gov/pubmed/18831779
http://dx.doi.org/10.1186/1471-2164-9-S2-S14
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