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We present a scheme to reverse-engineer gene networks on a genome-wide scale using a relatively small amount of gene expression data from microarray experiments. It uses singular value decomposition to construct a family of solutions and then identify the unique solution by robust regression. Our algorithm has $O(\log N)$ sampling complexity and $O(N^4)$ computational complexity. We test and validate our approach in a series of numerical experiments on model gene networks.

(3:45 Refreshments will be served before the talk in 551 Kerr Hall)