  A very important topic in systems biology is developing statistical methods
that automatically find causal relations in gene regulatory networks with no
prior knowledge of causal connectivity. Many methods have been developed for
time series data. However, discovery methods based on steady-state data are
often necessary and preferable since obtaining time series data can be more
expensive and/or infeasible for many biological systems. A conventional
approach is causal Bayesian networks. However, estimation of Bayesian networks
is ill-posed. In many cases it cannot uniquely identify the underlying causal
network and only gives a large class of equivalent causal networks that cannot
be distinguished between based on the data distribution. We propose a new
discovery algorithm for uniquely identifying the underlying causal network of
genes. To the best of our knowledge, the proposed method is the first algorithm
for learning gene networks based on a fully identifiable causal model called
LiNGAM. We here compare our algorithm with competing algorithms using
artificially-generated data, although it is definitely better to test it based
on real microarray gene expression data.
