  The high dimensional nature of genomics data complicates feature selection,
in particular in low sample size studies - not uncommon in clinical prediction
settings. It is widely recognized that complementary data on the features,
`co-data', may improve results. Examples are prior feature groups or p-values
from a related study. Such co-data are ubiquitous in genomics settings due to
the availability of public repositories. Yet, the uptake of learning methods
that structurally use such co-data is limited. We review guided adaptive
shrinkage methods: a class of regression-based learners that use co-data to
adapt the shrinkage parameters, crucial for the performance of those learners.
We discuss technical aspects, but also the applicability in terms of types of
co-data that can be handled. This class of methods is contrasted with several
others. In particular, group-adaptive shrinkage is compared with the
better-known sparse group-lasso by evaluating feature selection. Finally, we
demonstrate the versatility of the guided shrinkage methodology by showing how
to `do-it-yourself': we integrate implementations of a co-data learner and the
spike-and-slab prior for the purpose of improving feature selection in genetics
studies.
