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.

The combination of experimental and computational analysis is advancing the complete characterization of complex biological processes such as the epithelial to mesenchymal transition. Computational studies can be performed both in terms of signal transduction networks, that describe gene expression regulation and single cells or population dynamics, and multiscale models that integrate cell–cell and cell–environment interactions, besides modeling migration patterns.