大数据
计算机科学
可持续生产
代谢工程
生化工程
人工智能
机器学习
生产(经济)
工程类
生物
数据挖掘
生物化学
宏观经济学
经济
酶
作者
Gi Bae Kim,Won Jun Kim,Hyun Uk Kim,Sang Yup Lee
标识
DOI:10.1016/j.copbio.2019.08.010
摘要
Systems metabolic engineering allows efficient development of high performing microbial strains for the sustainable production of chemicals and materials. In recent years, increasing availability of bio big data, for example, omics data, has led to active application of machine learning techniques across various stages of systems metabolic engineering, including host strain selection, metabolic pathway reconstruction, metabolic flux optimization, and fermentation. In this paper, recent contributions of machine learning approaches to each major step of systems metabolic engineering are discussed. As the use of machine learning in systems metabolic engineering will become more widespread in accordance with the ever-increasing volume of bio big data, future prospects are also provided for the successful applications of machine learning.
科研通智能强力驱动
Strongly Powered by AbleSci AI