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We construct a gene-to-gene regulatory network from time-series data of expression levels for the whole genome of the yeast Saccharomyces cerevisae, in a case where the number of measurements is much smaller than the number of genes in the network. This network is analyzed with respect to present biological knowledge of all genes (according to the Gene Ontology database), and we find some of its large-scale properties to be in accordance with known facts about the organism. The linear modeling employed here has been explored several times, but due to lack of any validation beyond investigating individual genes, it has been seriously questioned with respect to its applicability to biological systems. Our results show the adequacy of the approach and make further investigations of the model meaningful.
Index Terms- Biology and genetics, time series analysis, network problems, gene network, network inference, Lasso, yeast, validation, outdegree.

M. Gustafsson, A. Lombardi and M. H?rnquist, "Constructing and Analyzing a Large-Scale Gene-to-Gene Regulatory Network-Lasso-Constrained Inference and Biological Validation," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 2, no. , pp. 254-261, 2005.
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