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Title: Classification of genes based on gene expression analysis

Abstract

Systems biology and bioinformatics are now major fields for productive research. DNA microarrays and other array technologies and genome sequencing have advanced to the point that it is now possible to monitor gene expression on a genomic scale. Gene expression analysis is discussed and some important clustering techniques are considered. The patterns identified in the data suggest similarities in the gene behavior, which provides useful information for the gene functionalities. We discuss measures for investigating the homogeneity of gene expression data in order to optimize the clustering process. We contribute to the knowledge of functional roles and regulation of E. coli genes by proposing a classification of these genes based on consistently correlated genes in expression data and similarities of gene expression patterns. A new visualization tool for targeted projection pursuit and dimensionality reduction of gene expression data is demonstrated.

Authors:
; ;  [1]
  1. Northumbria University (United Kingdom)
Publication Date:
OSTI Identifier:
21402595
Resource Type:
Journal Article
Resource Relation:
Journal Name: Physics of Atomic Nuclei; Journal Volume: 71; Journal Issue: 5; Other Information: DOI: 10.1134/S1063778808050025; Copyright (c) 2008 Pleiades Publishing, Ltd.
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; BIOLOGY; CLASSIFICATION; DNA; GENES; NUCLEIC ACIDS; ORGANIC COMPOUNDS

Citation Formats

Angelova, M., E-mail: maia.angelova@unn.ac.uk, Myers, C., E-mail: chris.myers@unn.ac.uk, and Faith, J., E-mail: joe.faith@unn.ac.u. Classification of genes based on gene expression analysis. United States: N. p., 2008. Web. doi:10.1134/S1063778808050025.
Angelova, M., E-mail: maia.angelova@unn.ac.uk, Myers, C., E-mail: chris.myers@unn.ac.uk, & Faith, J., E-mail: joe.faith@unn.ac.u. Classification of genes based on gene expression analysis. United States. doi:10.1134/S1063778808050025.
Angelova, M., E-mail: maia.angelova@unn.ac.uk, Myers, C., E-mail: chris.myers@unn.ac.uk, and Faith, J., E-mail: joe.faith@unn.ac.u. 2008. "Classification of genes based on gene expression analysis". United States. doi:10.1134/S1063778808050025.
@article{osti_21402595,
title = {Classification of genes based on gene expression analysis},
author = {Angelova, M., E-mail: maia.angelova@unn.ac.uk and Myers, C., E-mail: chris.myers@unn.ac.uk and Faith, J., E-mail: joe.faith@unn.ac.u},
abstractNote = {Systems biology and bioinformatics are now major fields for productive research. DNA microarrays and other array technologies and genome sequencing have advanced to the point that it is now possible to monitor gene expression on a genomic scale. Gene expression analysis is discussed and some important clustering techniques are considered. The patterns identified in the data suggest similarities in the gene behavior, which provides useful information for the gene functionalities. We discuss measures for investigating the homogeneity of gene expression data in order to optimize the clustering process. We contribute to the knowledge of functional roles and regulation of E. coli genes by proposing a classification of these genes based on consistently correlated genes in expression data and similarities of gene expression patterns. A new visualization tool for targeted projection pursuit and dimensionality reduction of gene expression data is demonstrated.},
doi = {10.1134/S1063778808050025},
journal = {Physics of Atomic Nuclei},
number = 5,
volume = 71,
place = {United States},
year = 2008,
month = 5
}
  • No abstract prepared.
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