深度学习
数据科学
计算机科学
人工智能
生物制造
桥(图论)
大数据
领域(数学)
数据挖掘
医学
生物
数学
遗传学
内科学
纯数学
出处
期刊:IEEE Journal of Biomedical and Health Informatics
[Institute of Electrical and Electronics Engineers]
日期:2019-01-23
卷期号:23 (3): 906-920
被引量:71
标识
DOI:10.1109/jbhi.2019.2894713
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.
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