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目标:一个自动语义机器学习微服务框架,支持生物医学和生物工程研究.

Hong Qing Yu1, Sam O'Neill1, Ali Kermanizadeh1

  • 1School of Computing and Human Sciences Research Centre, University of Derby, Derby DE22 3AW, UK.

Bioengineering (Basel, Switzerland)
|October 28, 2023
PubMed
概括

自动语义机器学习微服务 (AIMS) 框架自动化了用于生物医学研究的机器学习. 它可以实现自我监督的学习和适应新任务和新数据,增强科学探索.

科学领域:

  • 生物医学信息学 生物医学信息学
  • 医疗保健中的机器学习
  • 计算生物学 计算生物学

背景情况:

  • 生物医学研究面临着将复杂数据与机器学习 (ML) 开发相结合的挑战.
  • 机器学习管道的自动化对于有效的生物医学数据分析和应用至关重要.

研究的目的:

  • 为了引入自动语义机器学习微服务 (AIMS) 框架.
  • 通过自动化和特定领域的本体学来解决生物医学研究的ML方面的挑战.
  • 为了实现自主监督的学习和在生物医学领域的ML模型的持续适应.

主要方法:

  • 开发了针对生物医学数据量身定制的ML服务的本体结构的AIMS框架.
  • 集成的领域知识,优先考虑的模型解释性,并确保有效的数据处理.
  • 利用强化学习和基于本体学的政策方案来进行自我监督的知识学习.

主要成果:

  • 在生物医学领域证明了ML过程的自动化.
  • 成功地将丰富的域名知识库与ML工作流程集成.
  • 通过案例研究,展示了ML模型自我学习和适应新任务和数据的能力.

结论:

关键词:
人工智能自动化的AI自动化生物医学 生物医学知识图表知识图表机器学习是机器学习.微服务就是微服务.语义网络服务 (SWS)

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  • 艾姆斯有效地自动化了生物医学研究中的ML管道,整合了领域知识.
  • 该框架使机器具有自我学习能力,提高了机器应对新生物医学挑战的适应能力.
  • 艾姆斯简化了研究程序,提高了医疗保健科学探索的质量.