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Network Medicine applies the science of complexity to integrate large amounts of biological data and build dynamic maps of the body's networks, aiming to distinguish health and disease more precisely. Utilising tools from statistical physics and artificial intelligence, it has enhanced the understanding of complex diseases, yet remains limited by uncertainties in the data and current models, which need to be expanded.
In the recent publication in "Cell Press - MED", "Challenges and opportunities in the network medicine of complex diseases", Valeria d'Andrea and Manlio De Domenico from the Department of Physics and Astronomy "Galileo Galilei" at the University of Padua, along with Joseph Loscalzo - one of the pioneers of network medicine - from the Department of Medicine at Harvard Medical School in Boston, propose a paradigm shift based on three assumptions: studying diseases as interconnected complex systems; accepting and integrating data uncertainty through probabilistic models; and incorporating physicochemical constraints to truly understand biological interactions.
As Manlio De Domenico points out, "network medicine primarily allows for a better understanding of the mechanisms of complex diseases", identifying more reliable biomarkers, new therapeutic targets, and opportunities for drug repositioning. Valeria d'Andrea, a researcher from the Department of Physics and Astronomy at the University of Padua, highlights how recent advances in networks and computational tools now allow for the description of the complexity of living systems like never before.
In this context, the Padua Center for Network Medicine and the University of Padua play a central role in promoting an interdisciplinary approach that paves the way for advanced tools, such as digital twins, for increasingly personalised medicine. "Network Medicine 2.0" thus represents "a necessary step" to transform the complexity of biomedical data into tangible benefits for doctors and patients.
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Network Medicine applies the science of complexity to integrate large amounts of biological data and build dynamic maps of the body's networks, aiming to distinguish health and disease more precisely. Utilising tools from statistical physics and artificial intelligence, it has enhanced the understanding of complex diseases, yet remains limited by uncertainties in the data and current models, which need to be expanded.
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As Manlio De Domenico points out, "network medicine primarily allows for a better understanding of the mechanisms of complex diseases", identifying more reliable biomarkers, new therapeutic targets, and opportunities for drug repositioning. Valeria d'Andrea, a researcher from the Department of Physics and Astronomy at the University of Padua, highlights how recent advances in networks and computational tools now allow for the description of the complexity of living systems like never before.
In this context, the Padua Center for Network Medicine and the University of Padua play a central role in promoting an interdisciplinary approach that paves the way for advanced tools, such as digital twins, for increasingly personalised medicine. "Network Medicine 2.0" thus represents "a necessary step" to transform the complexity of biomedical data into tangible benefits for doctors and patients.
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