Big biomedical data as the key resource for discovery science

被引：50

作者：

Toga, Arthur W. ^{[1
]}

Foster, Ian ^{[2
,3
]}

Kesselman, Carl ^{[4
]}

Madduri, Ravi ^{[2
,3
]}

Chard, Kyle ^{[2
,3
]}

Deutsch, Eric W. ^{[5
]}

Price, Nathan D. ^{[5
]}

Glusman, Gustavo ^{[5
]}

Heavner, Benjamin D. ^{[5
]}

Dinov, Ivo D. ^{[6
]}

Ames, Joseph ^{[1
]}

Van Horn, John ^{[1
]}

Kramer, Roger ^{[5
]}

Hood, Leroy ^{[5
]}

机构：

[1] Univ So Calif, Lab Neuro Imaging, USC Stevens Neuroimaging & Informat Inst, Los Angeles, CA USA

[2] Univ Chicago, Computat Inst, Chicago, IL 60637 USA

[3] Argonne Natl Lab, Chicago, IL USA

[4] Univ So Calif, Inst Informat Sci, Los Angeles, CA USA

[5] Inst Syst Biol, Seattle, WA USA

[6] Univ Michigan, SOCR, UMSN, Ann Arbor, MI 48109 USA

来源：

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION | 2015年 / 22卷 / 06期

基金：

美国国家卫生研究院;

关键词：

big; biomedical; data; resource; discovery; science; neuroscience (ja); big data; analytics; BD2K; discovery science; Parkinson's disease; Alzheimer's disease (ID); STATISTICAL-MODEL; VISUALIZATION;

D O I：

10.1093/jamia/ocv077

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Modern biomedical data collection is generating exponentially more data in a multitude of formats. This flood of complex data poses significant opportunities to discover and understand the critical interplay among such diverse domains as genomics, proteomics, metabolomics, and phenomics, including imaging, biometrics, and clinical data. The Big Data for Discovery Science Center is taking an-ome to home" approach to discover linkages between these disparate data sources by mining existing databases of proteomic and genomic data, brain images, and clinical assessments. In support of this work, the authors developed new technological capabilities that make it easy for researchers to manage, aggregate, manipulate, integrate, and model large amounts of distributed data. Guided by biological domain expertise, the Center's computational resources and software will reveal relationships and patterns, aiding researchers in identifying biomarkers for the most confounding conditions and diseases, such as Parkinson's and Alzheimer's.

引用

页码：1126 / 1131

页数：6

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