Classification of genes based on gene expression analysis

Maia Angelova, Chris Myers, Joe Faith

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)780-787
JournalPhysics of Atomic Nuclei
Issue number5
Publication statusPublished - May 2008


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