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生物医学科学中的超维计算:简要的回顾

Fabio Cumbo¹, Davide Chicco^2,3

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

PeerJ. Computer science

|June 26, 2025

概括

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.

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.

关键词:

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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