Extracting Knowledge from Dynamics in Gene Expression

Most investigations of coordinated gene expression have focused on identifying correlated expression patterns between genes by examining their normalized static expression levels. In this study, we focus on the dynamics of gene expression by seeking to identify correlated patterns of changes in gene...

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Bibliographic Details
Published in:Journal of biomedical informatics 2001-02, Vol.34 (1), p.15-27
Main Authors: Reis, Ben Y, Butte, Atul S, Kohane, Isaac S
Format: Article
Language:English
Subjects:
bioinformatics
clinical informatics
Cluster Analysis
clustering
Computational Biology
dynamics
Gene Expression
Genes, Fungal
Saccharomyces cerevisiae - genetics
trends
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Summary:Most investigations of coordinated gene expression have focused on identifying correlated expression patterns between genes by examining their normalized static expression levels. In this study, we focus on the dynamics of gene expression by seeking to identify correlated patterns of changes in genetic expression level. In doing so, we build upon methods developed in clinical informatics to detect temporal trends of laboratory and other clinical data. We construct relevance networks from Saccharomyces cerevisiae gene-expression dynamics data and find genes with related functional annotations grouped together. While some of these associations are also found using a standard expression level analysis, many are identified exclusively through the dynamic analysis. These results strongly suggest that the analysis of gene expression dynamics is a necessary and important tool for studying regulatory and other functional relationships among genes. The source code developed for this investigation is freely available to all noncommercial investigators by contacting the authors.
ISSN:1532-0464
1532-0480
DOI:10.1006/jbin.2001.1005