We present a technique to characterize differentially expressed genes in
terms of their position in a high-dimensional co-expression network. The set-up
of Gaussian graphical models is used to construct representations of the
co-expression network in such a way that redundancy and the propagation of
spurious information along the network are avoided. The proposed inference
procedure is based on the minimization of the Bayesian Information Criterion
(BIC) in the class of decomposable graphical models.