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大数据时代的转化医学。

Translational medicine in the Age of Big Data.

机构信息

Departments of Biomedical Informatics, Systems Biology, and Medicine, Columbia University, New York, NY, USA.

出版信息

Brief Bioinform. 2019 Mar 22;20(2):457-462. doi: 10.1093/bib/bbx116.

Abstract

The ability to collect, store and analyze massive amounts of molecular and clinical data is fundamentally transforming the scientific method and its application in translational medicine. Collecting observations has always been a prerequisite for discovery, and great leaps in scientific understanding are accompanied by an expansion of this ability. Particle physics, astronomy and climate science, for example, have all greatly benefited from the development of new technologies enabling the collection of larger and more diverse data. Unlike medicine, however, each of these fields also has a mature theoretical framework on which new data can be evaluated and incorporated-to say it another way, there are no 'first principals' from which a healthy human could be analytically derived. The worry, and it is a valid concern, is that, without a strong theoretical underpinning, the inundation of data will cause medical research to devolve into a haphazard enterprise without discipline or rigor. The Age of Big Data harbors tremendous opportunity for biomedical advances, but will also be treacherous and demanding on future scientists.

摘要

收集、存储和分析大量分子和临床数据的能力正在从根本上改变科学方法及其在转化医学中的应用。收集观察结果一直是发现的前提,而科学理解的巨大飞跃伴随着这种能力的扩展。例如,粒子物理学、天文学和气候科学都从新技术的发展中受益匪浅,这些新技术使人们能够收集更大、更多样化的数据。然而,与医学不同的是,这些领域中的每一个都有一个成熟的理论框架,可以对新数据进行评估和整合——换句话说,没有可以从健康人体中分析得出的“第一原理”。人们担心,如果没有强大的理论基础,数据的泛滥将导致医学研究演变成一种没有纪律或严谨性的随意性事业。大数据时代为生物医学的进步带来了巨大的机会,但对未来的科学家来说也将是充满挑战和要求的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a277/6433900/c921b0b8720e/bbx116f1.jpg

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