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<p>Computational medicine uses mathematical modelling, high-performance computing, and the availability of large-scale biomedical data to study multiscale complex diseases. This review synthesises recent developments in in silico oncology, neurology, and epidemiology, highlighting their shared methodological foundations in systems theory and complex networks. We discuss how combining mechanistic approaches with machine learning integrates heterogeneous data. Finally, we identify translation barriers and outline future directions like digital twins for predictive, personalised healthcare.</p>

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In silico models in oncology, neurology, and epidemiology: systems-level and multiscale perspectives

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月22日

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  • Matteo Italia,
  • José Garcia Otero,
  • Juan Jiménez-Sánchez,
  • Fabio Dercole,
  • Juan Belmonte-Beitia

摘要

Computational medicine uses mathematical modelling, high-performance computing, and the availability of large-scale biomedical data to study multiscale complex diseases. This review synthesises recent developments in in silico oncology, neurology, and epidemiology, highlighting their shared methodological foundations in systems theory and complex networks. We discuss how combining mechanistic approaches with machine learning integrates heterogeneous data. Finally, we identify translation barriers and outline future directions like digital twins for predictive, personalised healthcare.