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Upstream Processing01:27

Upstream Processing

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Upstream processing represents a critical phase in biomanufacturing, wherein biological systems such as microorganisms, mammalian cells, or insect cells are cultivated to produce therapeutic proteins, vaccines, enzymes, or other biologically derived products. This phase encompasses all steps from the selection and genetic manipulation of the production organism to the cultivation of cells in bioreactors under tightly controlled environmental conditions.Host Selection and Genetic OptimizationThe...
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相关实验视频

Updated: Apr 7, 2026

Use of High-Throughput Automated Microbioreactor System for Production of Model IgG1 in CHO Cells
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针对小数据和上游生物处理应用的机器学习方法:综合性审查

Johnny Peng1, Thanh Tung Khuat1, Katarzyna Musial1

  • 1Complex Adaptive Systems Laboratory, The Data Science Institute, University of Technology Sydney, NSW 2007, Australia.

Biotechnology advances
|November 19, 2025
PubMed
概括

机器学习 (ML) 方法可以克服生物制药上游生物处理中的数据限制. 本综述对小型数据集有效的ML技术进行了分类和分析,为数据受限研究提供了实际指导.

科学领域:

  • 生物技术是生物技术.
  • 生物加工是一种生物加工.
  • 机器学习 机器学习

背景情况:

  • 在生物制药上游生物处理中,为机器学习 (ML) 获取大型数据集是具有挑战性和昂贵的.
  • 复杂的细胞培养和优化过程产生有限的数据,阻碍了ML应用.

研究的目的:

  • 审查和分类解决生物处理中小数据挑战的ML方法.
  • 为在数据受限的生物制药研究中应用ML提供实际指导.

主要方法:

  • 对小数据的ML方法的文献综述.
  • 将方法分类到一个分类学.
  • 分析核心概念和每个方法的有效性.

主要成果:

  • 开发了一种针对小数据挑战的ML方法分类.
  • 用生物处理示例评估了各种ML技术的有效性.
  • 确定了对数据受限生物处理的ML的研究缺口.

结论:

  • 机器学习为上游生物处理中的数据稀缺性提供了解决方案.
  • 审查的方法和分类学为研究人员提供了可操作的见解.
关键词:
生物制药产品 生物制药产品生物处理是生物过程.正在时间的学习学习.机器学习是机器学习.在线学习在线学习.小数据是小数据.

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  • 需要进一步的研究来优化ML在数据有限的生物制药开发.