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Crop cultivation has a long history in human civilization, with records showing the cultivation of cereal plants beginning at around 8000 BC. This early plant breeding was developed primarily to provide a steady supply of food.
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Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
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生物工艺工程中的转移学习方法:机遇和挑战

Daniel Barón Díaz1, Anna-Lena Drommershausen1, Alexander Grünberger1

  • 1Institute of Process Engineering in Life Sciences, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.

Biotechnology and bioengineering
|March 9, 2026
PubMed
概括

转移学习 (TL) 通过重复使用模型和数据来解决生物工艺工程中的数据稀缺问题. 这种方法加快了开发速度,并改善了有限数据场景的模型准确性和稳定性.

科学领域:

  • 生物工艺工程 生物工艺工程
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 数据稀缺是生物工艺工程的一个主要挑战,阻碍了模型开发和过程优化.
  • 传统的建模方法往往需要大量的数据集,而这些数据集在生物处理中很难和昂贵地获得.
  • 转移学习 (TL) 提供了一种新的解决方案,利用现有知识,以有限的数据构建有效的模型.

研究的目的:

  • 批判性地审查在生物工艺工程中应用转移学习 (TL) 的最新进展.
  • 突出TL在各种生物工艺领域的多样化应用.
  • 为了确定当前的挑战和未来的研究方向在这个领域的TL.

主要方法:

  • 关于生物工艺工程中转移学习应用的最新文献的综述.
  • 分析TL对基因组分析,生物反应器建模和染色学过程的影响.
  • 评估诸如数据异质性和模型可转移性等挑战.

主要成果:

  • TL显著提高模型准确性,用于预测蛋白质功能,生长和产品形成.
  • 在染色学过程中,TL提高了保留时间的预测.
  • 应用范围从上游 (基因组学) 到下游 (染色学) 生物处理.
关键词:
生物工艺工程是生物工艺工程.数据稀缺性 数据稀缺性混合型建模混合型建模机器学习是机器学习.转移学习转移学习

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结论:

  • 转移学习是克服生物工艺工程中的数据稀缺性的强大工具.
  • 未来的工作重点应该是将TL与混合和基于物理的模型集成在一起,并开发标准化数据集.
  • TL促进了创建更有效的数据,可概括和可解释的生物工艺模型.