Casual Compressive Sensing for Gene Network Inference
Quantitative Methods
2015-05-28 v1 Molecular Networks
Abstract
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.
Cite
@article{arxiv.1202.5678,
title = {Casual Compressive Sensing for Gene Network Inference},
author = {Mo Deng and Amin Emad and Olgica Milenkovic},
journal= {arXiv preprint arXiv:1202.5678},
year = {2015}
}