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Classification of Genes and Putative Biomarker Identification Using Distribution Metrics on Expression Profiles

BACKGROUND: Identification of genes with switch-like properties will facilitate discovery of regulatory mechanisms that underlie these properties, and will provide knowledge for the appropriate application of Boolean networks in gene regulatory models. As switch-like behavior is likely associated wi...

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Detalles Bibliográficos
Autores principales: Huang, Hung-Chung, Jupiter, Daniel, VanBuren, Vincent
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2816221/
https://www.ncbi.nlm.nih.gov/pubmed/20140228
http://dx.doi.org/10.1371/journal.pone.0009056
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author Huang, Hung-Chung
Jupiter, Daniel
VanBuren, Vincent
author_facet Huang, Hung-Chung
Jupiter, Daniel
VanBuren, Vincent
author_sort Huang, Hung-Chung
collection PubMed
description BACKGROUND: Identification of genes with switch-like properties will facilitate discovery of regulatory mechanisms that underlie these properties, and will provide knowledge for the appropriate application of Boolean networks in gene regulatory models. As switch-like behavior is likely associated with tissue-specific expression, these gene products are expected to be plausible candidates as tissue-specific biomarkers. METHODOLOGY/PRINCIPAL FINDINGS: In a systematic classification of genes and search for biomarkers, gene expression profiles (GEPs) of more than 16,000 genes from 2,145 mouse array samples were analyzed. Four distribution metrics (mean, standard deviation, kurtosis and skewness) were used to classify GEPs into four categories: predominantly-off, predominantly-on, graded (rheostatic), and switch-like genes. The arrays under study were also grouped and examined by tissue type. For example, arrays were categorized as ‘brain group’ and ‘non-brain group’; the Kolmogorov-Smirnov distance and Pearson correlation coefficient were then used to compare GEPs between brain and non-brain for each gene. We were thus able to identify tissue-specific biomarker candidate genes. CONCLUSIONS/SIGNIFICANCE: The methodology employed here may be used to facilitate disease-specific biomarker discovery.
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spelling pubmed-28162212010-02-07 Classification of Genes and Putative Biomarker Identification Using Distribution Metrics on Expression Profiles Huang, Hung-Chung Jupiter, Daniel VanBuren, Vincent PLoS One Research Article BACKGROUND: Identification of genes with switch-like properties will facilitate discovery of regulatory mechanisms that underlie these properties, and will provide knowledge for the appropriate application of Boolean networks in gene regulatory models. As switch-like behavior is likely associated with tissue-specific expression, these gene products are expected to be plausible candidates as tissue-specific biomarkers. METHODOLOGY/PRINCIPAL FINDINGS: In a systematic classification of genes and search for biomarkers, gene expression profiles (GEPs) of more than 16,000 genes from 2,145 mouse array samples were analyzed. Four distribution metrics (mean, standard deviation, kurtosis and skewness) were used to classify GEPs into four categories: predominantly-off, predominantly-on, graded (rheostatic), and switch-like genes. The arrays under study were also grouped and examined by tissue type. For example, arrays were categorized as ‘brain group’ and ‘non-brain group’; the Kolmogorov-Smirnov distance and Pearson correlation coefficient were then used to compare GEPs between brain and non-brain for each gene. We were thus able to identify tissue-specific biomarker candidate genes. CONCLUSIONS/SIGNIFICANCE: The methodology employed here may be used to facilitate disease-specific biomarker discovery. Public Library of Science 2010-02-04 /pmc/articles/PMC2816221/ /pubmed/20140228 http://dx.doi.org/10.1371/journal.pone.0009056 Text en Huang et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Huang, Hung-Chung
Jupiter, Daniel
VanBuren, Vincent
Classification of Genes and Putative Biomarker Identification Using Distribution Metrics on Expression Profiles
title Classification of Genes and Putative Biomarker Identification Using Distribution Metrics on Expression Profiles
title_full Classification of Genes and Putative Biomarker Identification Using Distribution Metrics on Expression Profiles
title_fullStr Classification of Genes and Putative Biomarker Identification Using Distribution Metrics on Expression Profiles
title_full_unstemmed Classification of Genes and Putative Biomarker Identification Using Distribution Metrics on Expression Profiles
title_short Classification of Genes and Putative Biomarker Identification Using Distribution Metrics on Expression Profiles
title_sort classification of genes and putative biomarker identification using distribution metrics on expression profiles
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2816221/
https://www.ncbi.nlm.nih.gov/pubmed/20140228
http://dx.doi.org/10.1371/journal.pone.0009056
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