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Digital twins (DTs) in precision medicine are increasingly viable, propelled by extensive data collection and advancements in artificial intelligence (AI), alongside traditional biomedical methodologies. We argue that including mechanistic simulations that produce behavior based on explicitly defined biological hypotheses and multiscale mechanisms is beneficial. It enables the exploration of diverse therapeutic strategies and supports dynamic clinical decision-making through insights from network science, quantitative biology, and digital medicine.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11742446 | PMC |
http://dx.doi.org/10.1038/s41746-024-01402-3 | DOI Listing |
Int J Surg
September 2025
First Clinical Medical College of Gannan Medical University, Ganzhou, Jiangxi, China.
J Invest Dermatol
September 2025
Department of Dermatology, CHU Nice, University Côte d'Azur, Nice, France; C3M, INSERM U1065, University Côte d'Azur, Nice, France.
Nat Med
September 2025
Freelance writer, Toronto, Ontario, Canada.
Clin Transl Med
September 2025
Department of Computer Science and Biomedical Engineering, Institute of Health Care Engineering with European Testing Center of Medical Devices, Graz University of Technology, Graz, Austria.
Computational modeling and simulation are playing an increasingly important role in oncology, bridging biological research, data science and clinical practice to better understand cancer complexity and inform therapeutic development. This special issue presents recent advances in multiscale modeling, artificial intelligence-driven systems, digital twins, and in silico trials, illustrating the evolving potential of computational tools to support innovation from bench to bedside. Together, these contributions outline a future in which precision medicine, adaptive therapies and personalized diagnostics are guided by integrative and predictive modeling approaches.
View Article and Find Full Text PDFChaos
September 2025
Department of Information Physics and Computing, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.
The increasing energy consumption required for information processing has become a significant challenge, leading to growing interest in optical and optoelectronic reservoir computing as a more efficient alternative. Trained reservoir computers are especially suited for low-energy applications near the edge. However, the computational cost of training the reservoir output weights, particularly due to matrix operations, adds potentially unwanted complexity to the architecture.
View Article and Find Full Text PDF