  Identifying measurable genetic indicators (or biomarkers) of a specific
condition of a biological system is a key element of precision medicine. Indeed
it allows to tailor diagnostic, prognostic and treatment choice to individual
characteristics of a patient. In machine learning terms, biomarker discovery
can be framed as a feature selection problem on whole-genome data sets.
However, classical feature selection methods are usually underpowered to
process these data sets, which contain orders of magnitude more features than
samples. This can be addressed by making the assumption that genetic features
that are linked on a biological network are more likely to work jointly towards
explaining the phenotype of interest. We review here three families of methods
for feature selection that integrate prior knowledge in the form of networks.
