Network Medicine in the age of biomedical big data

TL;DR

This review discusses how integrating biomedical big data with network medicine approaches can enhance understanding of disease mechanisms and aid in discovering therapeutic targets, advancing personalized healthcare.

Contribution

It provides a comprehensive survey of network types, data sources, and application paradigms in network medicine, highlighting opportunities and challenges in the field.

Findings

Network medicine combined with biomedical data aids disease characterization.

Application paradigms include protein-protein, expression-based, and gene regulatory networks.

Examples demonstrate successful network-based disease insights.

Abstract

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.