<p>This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p> <p>The use of DNA microarrays opens up the possibility of measuring the expression levels of thousands of genes simultaneously under different conditions. Time-course
experiments allow researchers to study the dynamics of gene interactions. The
inference of genetic networks from such measures can give important insights for the
understanding of a variety of biological problems. Most of the existing methods for
genetic network reconstruction require many experimental data points, or can only
be applied to the reconstruction of small subnetworks. Here we present a method that
reduces the dimensionality of the dataset and then extracts the significant dynamic
correlations among genes. The method requires a number of points achievable in
common time-course experiments.</p>
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Comp Funct Genomics. 2003 Feb; 4(1):148-154