  Network medicine is an emerging area of research dealing with molecular and
genetic interactions, network biomarkers of disease, and therapeutic target
discovery. Large-scale biomedical data generation offers a unique opportunity
to assess the effect and impact of cellular heterogeneity and environmental
perturbations on the observed phenotype. Marrying the two, network medicine
with biomedical data provides a framework to build meaningful models and
extract impactful results at a network level. In this review, we survey
existing network types and biomedical data sources. More importantly, we delve
into ways in which the network medicine approach, aided by phenotype-specific
biomedical data, can be gainfully applied. We provide three paradigms, mainly
dealing with three major biological network archetypes: protein-protein
interaction, expression-based, and gene regulatory networks. For each of these
paradigms, we discuss a broad overview of philosophies under which various
network methods work. We also provide a few examples in each paradigm as a test
case of its successful application. Finally, we delineate several opportunities
and challenges in the field of network medicine. Taken together, the
understanding gained from combining biomedical data with networks can be useful
for characterizing disease etiologies and identifying therapeutic targets,
which, in turn, will lead to better preventive medicine with translational
impacts on personalized healthcare.
