50 research outputs found
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