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We propose a novel framework for studying causal inference of geneinteractions using a combination of compressive sensing and Granger causalitytechniques. The gist of the approach is to discover sparse linear dependenciesbetween time series of gene expressions via a Granger-type elimination method.The method is tested on the Gardner dataset for the SOS network in E. coli, forwhich both known and unknown causal relationships are discovered.

Casual Compressive Sensing for Gene Network Inference