Casual Compressive Sensing for Gene Network Inference
  We propose a novel framework for studying causal inference of gene interactions using a combination of compressive sensing and Granger causality techniques. The gist of the approach is to discover sparse linear dependencies between 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, for which both known and unknown causal relationships are discovered. 
Mo Deng, Amin Emad, and Olgica Milenkovic
q-bio.QM q-bio.MN