Network medicine can advance current medical practice by arising as response to the limitations of a reductionist approach focusing on cardiovascular (CV) diseases as a direct consequence of a single defect. This molecular-bioinformatic approach integrates heterogeneous "omics" data and artificial intelligence to identify a chain of perturbations involving key components of multiple molecular networks that are closely related in the human interactome. The clinical view of the network-based approach is greatly supported by the general law of molecular interconnection governing all biological complex systems. Recent advances in bioinformatics have culminated in numerous quantitative platforms able to identify CV disease modules underlying perturbations of the interactome. This might provide novel insights in CV disease mechanisms as well as putative biomarkers and drug targets. We describe the network-based principles and discuss their application to classifying and treating common CV diseases. We compare the strengths and weaknesses of molecular networks in comparison with the classical current reductionist approach, and remark on the necessity for a two-way approach connecting network medicine with large clinical trials to concretely translate novel insights from bench to bedside.
Keywords: Artificial intelligence; Cardiovascular diseases; Network medicine; Personalized therapy; Precision medicine.