Graphical Gaussian models are popular tools for the estimation of
(undirected) gene association networks from microarray data. A key issue when
the number of variables greatly exceeds the number of samples is the estimation
of the matrix of partial correlations. Since the (Moore-Penrose) inverse of the
sample covariance matrix leads to poor estimates in this scenario, standard
methods are inappropriate and adequate regularization techniques are needed.