在微生物组研究和工程中的机器学习
Ryan De Sotto1,2,3,4, Nikhil Aggarwal1,2,3,4, Elizabeth Huiwen Tham1,2,5,6
1NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore 117456, Singapore.
ACS synthetic biology
|December 22, 2025
概括
机器学习 (ML) 通过分析复杂的微生物群落和预测相互作用来推进微生物组工程. 这使得针对医疗保健和农业进行有针对性的操纵,克服了未来应用的数据限制.
科学领域:
- 微生物组研究的研究.
- 计算生物学是一种计算生物学.
- 合成生物学 合成生物学
背景情况:
- 微生物组对于医疗保健,农业和生物生产至关重要.
- 工程微生物组需要理解复杂的微生物相互作用.
- 机器学习 (ML) 为分析微生物组数据提供了强大的工具.
研究的目的:
- 检查ML在微生物组工程中的变革性应用.
- 突出ML在微生物组数据分析和有针对性的操纵中的作用.
- 讨论ML驱动微生物组工程中的挑战和未来前景.
主要方法:
- 使用ML算法来分析大型微生物组数据集.
- 预测微生物相互作用和社区行为.
- 采用诸如定数感应分子操纵,抗微生物和细菌菌体等策略进行向操纵.
主要成果:
- 在识别微生物群落和预测相互作用方面,ML已经取得了成功.
- 机器学习有助于优化微生物社区设计以满足特定功能.
- 机器学习与实验方法的整合正在推进微生物组工程.
结论:
- 机器学习正在彻底改变微生物组工程,使新的应用成为可能.
- 挑战包括数据限制,生物复杂性和整合计算/实验方法.
- 未来数据生成,算法和协作方面的进步将释放微生物组的潜力.
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