Microarray Expression Profile Analysis

There is a reasonably large amount of microarray expression data available in the public domain. Gene expression clustering is useful in addressing these datasets in at least three ways: (1) extraction of regulatory motifs (coregulation from coexpres-sion); (2) inference of functional annotation; and (3) as a molecular signature in distinguishing cell or tissue types. In this approach, the gene expression data are utilized to find functional hits for an unvalidated gene or a hypothetical protein through expression profile comparison across all experiments. An expression profile is usually a 2D or 3D matrix associated with time points, doses, and chemicals. Functional hits (close neighbours) can be detected by comparing the expression profile of a query with that of all other known genes.

Fig. 10.6 The architecture of the novel bioinformatics system for annotating (invalidated genes) and hypothetical proteins.
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