Molecular networks in Network Medicine: Development and applications

Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein–protein interaction networks, correlation‐based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseases. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Translational, Genomic, and Systems Medicine > Translational Medicine Analytical and Computational Methods > Analytical Methods Analytical and Computational Methods > Computational Methods The Visual Analytics cycle applied to Network Medicine. Data from different domains (e.g., cellular, molecular, and genetic networks) are input to two different processes, Visual Data Exploration which exploits visualization paradigms (Node‐Edge, Matrix, Chords, etc.) to represent these data and classic Automated Data Analysis through different approaches (machine learning, network analysis algorithms, etc.). These two processes are interconnected, allowing an analyst to steer algorithms by interacting with the visual representation of results. The whole process generates new insights (e.g., relationships among networks) used as a feedback loop for new cycles of analysis.