01309nas a2200145   4500008004100000245010100041210006900142300001100211490000600222520077700228100001601005700002101021700001501042856010601057       2003                        eng d00aAn approach to inferring transcriptional regulation among genes from large-scale expression data0 aapproach to inferring transcriptional regulation among genes fro  a148-540 v43 aThe 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.1 aHerrero, J.1 aDiaz-Uriarte, R.1 aDopazo, J.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18629097