Next-Generation Machine Learning for Biological Networks

被引:705
作者
Camacho, Diogo M. [1 ]
Collins, Katherine M. [1 ,2 ,3 ]
Powers, Rani K. [4 ]
Costello, James C. [4 ]
Collins, James J. [1 ,5 ,6 ,7 ]
机构
[1] Harvard Univ, Wyss Inst Biol Inspired Engn, Boston, MA 02115 USA
[2] MIT, Dept Brain & Cognit Sci, E25-618, Cambridge, MA 02139 USA
[3] MIT, Dept Elect Engn & Comp Sci, Cambridge, MA 02139 USA
[4] Univ Colorado, Dept Pharmacol, Computat Biosci Program, Anschutz Med Campus, Aurora, CO 80045 USA
[5] MIT, Dept Biol Engn, 77 Massachusetts Ave, Cambridge, MA 02139 USA
[6] MIT, Inst Med Engn & Sci, 77 Massachusetts Ave, Cambridge, MA 02139 USA
[7] Broad Inst MIT & Harvard, Cambridge, MA 02142 USA
关键词
SYNTHETIC BIOLOGY; DRUG-SENSITIVITY; PREDICTION; DISCOVERY; SELECTION; RESOURCE; DISEASE; MODELS; COMMUNITIES; CHALLENGE;
D O I
10.1016/j.cell.2018.05.015
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
070307 [化学生物学]; 071010 [生物化学与分子生物学];
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.
引用
收藏
页码:1581 / 1592
页数:12
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