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数据存储库能否帮助找到针对复杂疾病的有效治疗方法?

Can data repositories help find effective treatments for complex diseases?

作者信息

Farber Gregory K

机构信息

Office of Technology Development and Coordination, National Institute of Mental Health, National Institutes of Health, 6001 Executive Boulevard, Room 7162, Rockville, MD 20892-9640, USA.

出版信息

Prog Neurobiol. 2017 May;152:200-212. doi: 10.1016/j.pneurobio.2016.03.008. Epub 2016 Mar 24.

Abstract

There are many challenges to developing treatments for complex diseases. This review explores the question of whether it is possible to imagine a data repository that would increase the pace of understanding complex diseases sufficiently well to facilitate the development of effective treatments. First, consideration is given to the amount of data that might be needed for such a data repository and whether the existing data storage infrastructure is enough. Several successful data repositories are then examined to see if they have common characteristics. An area of science where unsuccessful attempts to develop a data infrastructure is then described to see what lessons could be learned for a data repository devoted to complex disease. Then, a variety of issues related to sharing data are discussed. In some of these areas, it is reasonably clear how to move forward. In other areas, there are significant open questions that need to be addressed by all data repositories. Using that baseline information, the question of whether data archives can be effective in understanding a complex disease is explored. The major goal of such a data archive is likely to be identifying biomarkers that define sub-populations of the disease.

摘要

开发针对复杂疾病的治疗方法面临诸多挑战。本综述探讨了是否有可能设想一个数据存储库,它能够充分加快对复杂疾病的理解速度,以促进有效治疗方法的开发。首先,考虑这样一个数据存储库可能需要的数据量以及现有的数据存储基础设施是否足够。接着研究了几个成功的数据存储库,看它们是否有共同特征。然后描述了一个在开发数据基础设施方面尝试失败的科学领域,以了解能从中学到哪些关于致力于复杂疾病的数据存储库的经验教训。随后,讨论了与数据共享相关的各种问题。在其中一些领域,如何推进相当明确。在其他领域,则存在所有数据存储库都需要解决的重大开放性问题。利用这些基础信息,探讨了数据存档在理解复杂疾病方面是否能发挥有效作用的问题。这样一个数据存档的主要目标可能是识别定义该疾病亚群的生物标志物。

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本文引用的文献

1
Human Genome Project: Twenty-five years of big biology.
Nature. 2015 Oct 1;526(7571):29-31. doi: 10.1038/526029a.
2
Development of a smartphone application for eating disorder self-monitoring.
Int J Eat Disord. 2015 Nov;48(7):972-82. doi: 10.1002/eat.22386. Epub 2015 Jul 27.
3
Amazon's Mechanical Turk: A New Source of Inexpensive, Yet High-Quality, Data?
Perspect Psychol Sci. 2011 Jan;6(1):3-5. doi: 10.1177/1745691610393980. Epub 2011 Feb 3.
4
Big Data: Astronomical or Genomical?
PLoS Biol. 2015 Jul 7;13(7):e1002195. doi: 10.1371/journal.pbio.1002195. eCollection 2015 Jul.
5
Biomarkers of treatment outcome in schizophrenia: Defining a benchmark for clinical significance.
Eur Neuropsychopharmacol. 2015 Oct;25(10):1578-85. doi: 10.1016/j.euroneuro.2015.06.008. Epub 2015 Jun 20.
6
Excess of rare, inherited truncating mutations in autism.
Nat Genet. 2015 Jun;47(6):582-8. doi: 10.1038/ng.3303. Epub 2015 May 11.
7
The BRAIN Initiative: developing technology to catalyse neuroscience discovery.
Philos Trans R Soc Lond B Biol Sci. 2015 May 19;370(1668). doi: 10.1098/rstb.2014.0164.
8
Interacting with the National Database for Autism Research (NDAR) via the LONI Pipeline workflow environment.
Brain Imaging Behav. 2015 Mar;9(1):89-103. doi: 10.1007/s11682-015-9354-z.
9
Common genetic variants influence human subcortical brain structures.
Nature. 2015 Apr 9;520(7546):224-9. doi: 10.1038/nature14101. Epub 2015 Jan 21.
10
GenBank.
Nucleic Acids Res. 2015 Jan;43(Database issue):D30-5. doi: 10.1093/nar/gku1216. Epub 2014 Nov 20.

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