  The inference of networks of dependencies by Gaussian Graphical models on
high-throughput data is an open issue in modern molecular biology. In this
paper we provide a comparative study of three methods to obtain small sample
and high dimension estimates of partial correlation coefficients: the
Moore-Penrose pseudoinverse (PINV), residual correlation (RCM) and
covariance-regularized method $(\ell_{2C})$. We first compare them on simulated
datasets and we find that PINV is less stable in terms of AUC performance when
the number of variables changes. The two regularized methods have comparable
performances but $\ell_{2C}$ is much faster than RCM. Finally, we present the
results of an application of $\ell_{2C}$ for the inference of a gene network
for isoprenoid biosynthesis pathways in Arabidopsis thaliana.
