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. 2015 Nov;22(6):1126-31.
doi: 10.1093/jamia/ocv077. Epub 2015 Jul 21.

Big biomedical data as the key resource for discovery science

Arthur W Toga  1 , Ian Foster  2 , Carl Kesselman  3 , Ravi Madduri  2 , Kyle Chard  2 , Eric W Deutsch  4 , Nathan D Price  4 , Gustavo Glusman  4 , Benjamin D Heavner  4 , Ivo D Dinov  5 , Joseph Ames  6 , John Van Horn  6 , Roger Kramer  4 , Leroy Hood  4
Affiliations

Big biomedical data as the key resource for discovery science

Arthur W Toga et al. J Am Med Inform Assoc. 2015 Nov.

Abstract

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.

Keywords: Alzheimer's disease (ID); BD2K; Parkinson's disease; analytics; big; big data; biomedical; data; discovery; discovery science; resource; science, neuroscience (ja).

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Figures

Figure 1:
Figure 1:
The Scoreboard provides a summary view of the data matching search criteria in the Global Alzheimer's Association Interactive Network (GAAIN) federated system. The far-left column shows each of GAAIN’s Data Partners. The top row shows the data attributes in GAAIN. A user can select any number of these data attributes to determine a collective total number of subject data available from GAAIN Data Partners.
Figure 2:
Figure 2:
Workflow representations of complex Trans-Proteomic Pipeline computational protocols implemented as platform-agnostic local, distributed, and cloud-based infrastructures.
Figure 3:
Figure 3:
LONI Quality Control (QC) system for high-throughput semi-supervised curation of multidimensional neuroimaging data.
See this image and copyright information in PMC

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