Cargando…
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...
Autores principales: | , , |
---|---|
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 |
_version_ | 1782177080402771968 |
---|---|
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. |
format | Text |
id | pubmed-2816221 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT huanghungchung classificationofgenesandputativebiomarkeridentificationusingdistributionmetricsonexpressionprofiles AT jupiterdaniel classificationofgenesandputativebiomarkeridentificationusingdistributionmetricsonexpressionprofiles AT vanburenvincent classificationofgenesandputativebiomarkeridentificationusingdistributionmetricsonexpressionprofiles |