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通过机器学习设计纳米异能材料.

Lang Rao1, Yuan Yuan2,3, Xi Shen4,5

  • 1Shenzhen Bay Laboratory, Shenzhen, China. lrao@szbl.ac.cn.

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概括

机器学习 (ML) 通过克服合成和临床翻译挑战,加速纳米异能技术的发展. 这种方法通过先进的人工智能和下一代纳米药物的可靠数据,承诺更安全,更有效的疾病管理.

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科学领域:

  • 纳米技术和纳米医学
  • 医疗保健中的人工智能
  • 药物输送和诊断 药物输送和诊断 药物输送和诊断

背景情况:

  • 传统的诊断和治疗方法在疾病管理中面临着固有的局限性.
  • 使用纳米技术的纳米化器具提供了更高的效率和安全性,但面临着采用障碍.
  • 挑战包括纳米粒子合成,理解纳米生物相互作用和临床翻译.

研究的目的:

  • 审查机器学习 (ML) 辅助的纳米异常技术的进展和挑战.
  • 讨论使用ML开发下一代纳米激光器的机会.
  • 突出ML在克服纳米otheranostic发展障碍的潜力.

主要方法:

  • 关于纳米异能学和机器学习应用的当前文献的综述.
  • 分析ML在解决合成,纳米生物相互作用和临床翻译挑战方面的作用.
  • 讨论对可靠数据集和先进的ML模型的要求.

主要成果:

  • 机器学习提供了工具,以加快耗时的任务在nanotheranostics开发.
  • 机器学习可以提高对纳米生物相互作用的理解,并有助于化学,制造和控制.
  • 已经取得了显著的进展,但广泛采用需要进一步的进展.

结论:

  • 机器学习辅助的纳米异常物代表了改善疾病管理的有希望的范式.
  • 解决数据可靠性和ML模型复杂性的挑战对于临床益处至关重要.
  • 机器学习的整合是释放纳米异能材料的全部潜力的关键.