Paper Information

Network medicine applies fundamental principles of complexity science and systems medicine to integrate and analyze complex structured data, including genomics, transcriptomics, proteomics, and metabolomics, to characterize the dynamical states of health and disease within biological networks. In this perspective, we discuss the major successes of the field and how incorporating techniques based on statistical physics and machine learning in network medicine has significantly refined our understanding of disease networks. Despite these achievements, the maturation of network medicine presents challenges that must be addressed. Limitations in defining biological units and interactions, interpreting network models, and accounting for experimental uncertainties hinder the field's progress. The next phase of network medicine must expand the current framework by incorporating more realistic assumptions about biological units and their interactions across multiple relevant scales. This expansion is crucial for advancing our understanding of complex diseases and improving strategies for their diagnosis, treatment, and prevention.