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深度学习:医学和技术中的当前和新兴应用。

Deep Learning: Current and Emerging Applications in Medicine and Technology.

出版信息

IEEE J Biomed Health Inform. 2019 May;23(3):906-920. doi: 10.1109/JBHI.2019.2894713. Epub 2019 Jan 23.

Abstract

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.

摘要

机器学习正在使研究人员能够分析和理解传统领域(如生物学、医学和工程学)以及新兴领域(如合成生物学、自动化化学合成和生物制造)中越来越复杂的物理和生物现象。这些领域需要新的范式来理解日益复杂的数据,并将这些数据转化为患者的医疗产品和服务。向深度学习和复杂建模的转变是试图弥合获取大量复杂数据与将这些数据转化为实际洞察力之间的差距。在这里,我们概述了机器学习领域,及其在传统和新兴领域的当前应用和需求,并讨论了使用深度学习来理解分子梭子群行为的一个说明性尝试。

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