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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
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Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing hydrogen spectra.
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量子计算通过分析复杂的多原子数据,为精密医学提供了一个新的范式. 这项技术有望加速药物发现,改善诊断,并通过克服经典计算的局限性来实现个性化治疗.

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科学领域:

  • 生物医学信息学 生物医学信息学
  • 量子计算是一种量子计算.
  • 精准医学是一门精准的医学.

背景情况:

  • 精准医学整合了多组数据 (遗传,表观遗传等). 用于量身定制的医疗保健.
  • 经典计算面临的挑战是多原子数据集的规模和复杂性.
  • 量子计算为分子模拟和高维数据分析提供了新的方法.

研究的目的:

  • 探索量子计算和精密医学的融合.
  • 突出诊断建模,多原子数据集成和药物发现中的应用.
  • 将量子技术转化为下一代精准医学的路线图概述.

主要方法:

  • 对用于疾病预测的量子机器学习的早期概念验证研究的审查.
  • 检查蛋白质折叠的量子算法和药物设计的生成模型.
  • 对基因网络推断和变异优先级的混合量子-经典工作流程的分析.

主要成果:

  • 量子机器学习显示了疾病预测的潜力.
  • 量子算法可以帮助蛋白质折叠和新药设计.
  • 混合方法正在使基因网络推断和多原子研究中的变异分析成为可能.

结论:

  • 量子计算对精密医学具有变革性的潜力,尽管目前的硬件和可扩展性有限.
  • 未来的量子模型可以实现生物系统的模拟,用于个性化治疗测试.
  • 量子硬件和算法的持续进步对于实现量子计算在生物医学上的全部影响至关重要.