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[计算机模拟方法加速从床边到实验台的反向转化研究]

[In Silico Approaches Accelerate Reverse Translational Research from Bedside to Bench].

作者信息

Okuno Yasushi

机构信息

Graduate School of Medicine, Kyoto University.

出版信息

Yakugaku Zasshi. 2017;137(6):691-695. doi: 10.1248/yakushi.16-00250-4.

DOI:10.1248/yakushi.16-00250-4
PMID:28566575
Abstract

Corresponding with accelerated computing power, such as that found in supercomputers, simulation and big data are becoming increasingly important to modern science, second only to experimental and theoretical sciences research. The field of medicine is said to have entered the era of big data, with significant progress in recent years in the development of increasingly sophisticated equipment for measurement, observation, and information and communication technology (ICT). In particular, greater precision in personalized medicine will require the analysis of a large quantity of individual genome sequences. Research and development of techniques to analyze big data with respect to individual genome sequences are an urgent need. In clinical medicine and epidemiology, the analysis of clinical big data or real-world data has attracted attention as a new approach, which can be applied to examining occurrences at an actual clinical site. In this review, we have discussed the challenges and potential of an in silico approach for reverse translational research.

摘要

随着超级计算机等加速计算能力的提升,模拟和大数据对现代科学变得越来越重要,仅次于实验科学和理论科学研究。医学领域据说已进入大数据时代,近年来在用于测量、观察的日益精密的设备以及信息通信技术(ICT)的发展方面取得了重大进展。特别是,个性化医疗的更高精准度将需要分析大量个体基因组序列。针对个体基因组序列分析大数据的技术研发迫在眉睫。在临床医学和流行病学中,临床大数据或真实世界数据的分析作为一种新方法已引起关注,可应用于在实际临床场所检查疾病的发生情况。在本综述中,我们讨论了计算机模拟方法在反向转化研究中的挑战和潜力。

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