生物
机器学习
人工智能
生物网络
药物发现
系统生物学
生物学数据
交叉口(航空)
深度学习
微生物群
计算机科学
数据科学
计算生物学
生物信息学
工程类
航空航天工程
作者
Diogo M. Camacho,Katherine M. Collins,Rani K. Powers,James C. Costello,James J. Collins
出处
期刊:Cell
[Elsevier]
日期:2018-06-01
卷期号:173 (7): 1581-1592
被引量:617
标识
DOI:10.1016/j.cell.2018.05.015
摘要
Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets and make predictions on likely outcomes, machine learning can be used to study complex cellular systems such as biological networks. Here, we provide a primer on machine learning for life scientists, including an introduction to deep learning. We discuss opportunities and challenges at the intersection of machine learning and network biology, which could impact disease biology, drug discovery, microbiome research, and synthetic biology. Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets and make predictions on likely outcomes, machine learning can be used to study complex cellular systems such as biological networks. Here, we provide a primer on machine learning for life scientists, including an introduction to deep learning. We discuss opportunities and challenges at the intersection of machine learning and network biology, which could impact disease biology, drug discovery, microbiome research, and synthetic biology.
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