机器学习和合成生物学中的深度学习:关键架构,应用和挑战
1Department of Chemistry, Natural and Applied Sciences, University of Wisconsin-Green Bay, Green Bay, Wisconsin 54311-7001, United States.
ACS omega
|March 11, 2024
概括
机器学习 (ML) 和深度学习 (DL) 正在彻底改变合成生物学,通过使新生物组件的设计和优化实验. 这些先进的计算工具加速了工程细胞,蛋白质和代谢途径的创新.
科学领域:
- 合成生物学 合成生物学
- 计算生物学是一种计算生物学.
- 生物技术是生物技术.
背景情况:
- 由于复杂的相互连接,生物系统的设计是复杂的.
- 机器学习 (ML) 和深度学习 (DL) 为生物设计提供了强大的工具.
- 合成生物学为ML/DL生成数据,而ML/DL指导合成生物学工作.
研究的目的:
- 审查ML/DL和合成生物学之间的协同作用.
- 突出ML/DL在工程细胞,蛋白质和代谢途径中的应用.
- 讨论将ML/DL与合成生物学集成的挑战和解决方案.
主要方法:
- 审查ML/DL基础和预测能力.
- 描述深度学习架构及其合成生物学应用.
- 对合成生物学中ML/DL的挑战和建议解决方案的分析.
主要成果:
- ML/DL允许设计新的生物部件和改进的实验策略.
- 应用包括自动化显微镜数据分析和蛋白质结构预测.
- 人工神经网络 (ANN) 的生物分子实现正在出现.
结论:
- 通过增强设计和发现,ML/DL显著推进了合成生物学.
- 整合ML/DL加速了复杂生物系统的工程.
- 应对当前的挑战将进一步释放ML/DL在生物技术中的潜力.
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