We propose a scheme to reverse-engineer gene networks on a genome-wide scale using a relatively small amount of gene expresion data from microarray experiments. Our method is based on the empirical observation that such networks are typically large and sparse. It uses singular value decomposition to construct a family of candidate solutions and then uses robust regression to identify the solution with the smallest number of connections as the most likely solution. Our algorithm has O(log N) sampling complexity and O(N4) computational complexity. We test and validate our approach in a series of in numero experiments on model gene networks.
|Original language||English (US)|
|Number of pages||6|
|Journal||Proceedings of the National Academy of Sciences of the United States of America|
|State||Published - Apr 30 2002|
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