Deep Learning: Current and Emerging Applications in Medicine and Technology

被引:57
作者
Akay, Altug [1 ]
Hess, Henry [1 ]
机构
[1] Columbia Univ, Dept Biomed Engn, New York, NY 10027 USA
基金
美国国家科学基金会;
关键词
Deep learning; machine learning; neural networks; informatics; NEURAL-NETWORKS; MOLECULAR SHUTTLES; MICROSCOPY IMAGES; SYNTHETIC BIOLOGY; ESCHERICHIA-COLI; MOTOR PROTEINS; DRUG RESPONSE; EEG; DRIVEN; IDENTIFICATION;
D O I
10.1109/JBHI.2019.2894713
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Machine learning is enabling researchers to analyze and understand increasingly complex physical and biological phenomena in traditional fields such as biology, medicine, and engineering and emerging fields like synthetic biology, automated chemical synthesis, and biomanufacturing. These fields require new paradigms toward understanding increasingly complex data and converting such data into medical products and services for patients. The move toward deep learning and complex modeling is an attempt to bridge the gap between acquiring massive quantities of complex data, and converting such data into practical insights. Here, we provide an overview of the field of machine learning, its current applications and needs in traditional and emerging fields, and discuss an illustrative attempt at using deep learning to understand swarm behavior of molecular shuttles.
引用
收藏
页码:906 / 920
页数:15
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