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相关概念视频

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An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
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Engineering Skeletal Muscle Tissues from Murine Myoblast Progenitor Cells and Application of Electrical Stimulation
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人工智能和机器学习应用用于培养肉类.

Michael E Todhunter1, Sheikh Jubair2, Ruchika Verma2

  • 1Todhunter Scientifics, Minneapolis, MN, United States.

Frontiers in artificial intelligence
|October 9, 2024
PubMed
概括

机器学习 (ML) 可以通过优化实验和减少资源需求来加速培养肉 (CM) 的开发. 本综述探讨了CM当前的ML应用以及未来的研究方向.

关键词:
人工智能的人工智能是人工智能.生物处理生物处理.细胞培养培养的细胞培养.文化 媒体 设计 设计培养肉的培养肉是什么食品科学 食品科学机器学习是机器学习.显微镜 显微镜是指使用显微镜.

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科学领域:

  • 食品科学 食品科学 食品科学
  • 生物技术是生物技术.
  • 计算机科学 计算机科学

背景情况:

  • 培养肉提供了环境,伦理和健康方面的好处,但面临着重大的技术障碍.
  • 机器学习 (ML) 为加速培养肉类 (CM) 的研发提供了机会.
  • 目前在CM中的ML应用是新生的,需要全面的概述.

研究的目的:

  • 审查关于在培养肉中使用ML的现有文献.
  • 确定ML可以解决CM生产当前挑战的关键领域.
  • 为CM和ML科学家提供跨学科研究的基础.

主要方法:

  • 在培养肉中使用ML的文献审查.
  • 分析ML在四个关键CM研发领域的作用:细胞系建立,媒体设计,图像分析和生物处理.
  • 对CM研究相关数据集的调查.

主要成果:

  • 机器学习可以简化实验,预测结果,并减少CM的研发时间和资源.
  • 在细胞系开发,媒介优化,图像分析和生物处理中确定了ML的机会.
  • 强调需要可访问的数据集来推进CM中的ML.

结论:

  • ML具有显著的潜力,可以加速培养肉技术的进步.
  • ML和CM科学家之间的跨学科合作对于未来的进步至关重要.
  • 需要进一步的研究和数据共享,以便在培养肉生产中充分利用ML.