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生物医学科学中的超维计算:简要的回顾

Fabio Cumbo1, Davide Chicco2,3

  • 1Center for Computational Life Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, Ohio, United States.

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概括
此摘要是机器生成的。

超维计算 (HDC) 为分析生物医学数据提供了一种新的方法. 这次审查批判性地检查了HDC.

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生物信息学是一种生物信息学.生物医学科学 生物医学科学化学信息学 化学信息学超维的计算超维的计算.医疗信息学医学信息学审查 审查 审查 审查矢量符号架构的架构是矢量符号架构.

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科学领域:

  • 生物医学信息学 生物医学信息学
  • 生物信息学是一种生物信息学.
  • 化学信息学 化学信息学
  • 医疗信息学 医疗信息学

背景情况:

  • 超维计算 (HDC),也称为矢量符号架构 (VSA),是一种新兴的计算范式.
  • HDC使用高维向量来表示和组合信息.
  • 它在各个科学领域都有广泛的应用,包括机器学习和人工智能.

研究的目的:

  • 提供对生物医学科学当前HDC格局的批判性和全面的审查.
  • 强调在生物信息学,化学信息学和医疗信息学中应用HDC的优缺点.
  • 确定在这些领域采用HDC的潜在未来方向和挑战.

主要方法:

  • 对约四十篇科学文章的系统审查 HDC 应用到生物医学数据.
  • 分析关键方面,包括矢量构造,数据编码和使用的编程语言.
  • 评估开放访问状态,软件代码的可用性,以及著名的研究小组,期刊和会议.

主要成果:

  • 确定了生物医学科学当前HDC应用中的关键特征和方法.
  • 量化了开放获取出版物和公开可用的软件代码的普及率.
  • 总结了跨作者,期刊和会议的研究贡献的分布.

结论:

  • HDC为生物医学数据分析提供了一个有前途的计算范式.
  • 该审查概述了HDC在特定生物医学领域的当前状态,优点和缺点.
  • 这项工作是第一个开放式调查,为生物医学科学研究人员提供了宝贵的见解.