  We consider the problem of jointly estimating multiple inverse covariance
matrices from high-dimensional data consisting of distinct classes. An
$\ell_2$-penalized maximum likelihood approach is employed. The suggested
approach is flexible and generic, incorporating several other
$\ell_2$-penalized estimators as special cases. In addition, the approach
allows specification of target matrices through which prior knowledge may be
incorporated and which can stabilize the estimation procedure in
high-dimensional settings. The result is a targeted fused ridge estimator that
is of use when the precision matrices of the constituent classes are believed
to chiefly share the same structure while potentially differing in a number of
locations of interest. It has many applications in (multi)factorial study
designs. We focus on the graphical interpretation of precision matrices with
the proposed estimator then serving as a basis for integrative or meta-analytic
Gaussian graphical modeling. Situations are considered in which the classes are
defined by data sets and subtypes of diseases. The performance of the proposed
estimator in the graphical modeling setting is assessed through extensive
simulation experiments. Its practical usability is illustrated by the
differential network modeling of 12 large-scale gene expression data sets of
diffuse large B-cell lymphoma subtypes. The estimator and its related
procedures are incorporated into the R-package rags2ridges.
