Big biomedical data as the key resource for discovery science

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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)1126-1131
Number of pages6
JournalJournal of the American Medical Informatics Association
Volume22
Issue number6
DOIs
StatePublished - 2015
Externally publishedYes

Keywords

  • Alzheimer's disease (ID)
  • Analytics
  • BD2K
  • Big
  • Big data
  • Biomedical
  • Data
  • Discovery
  • Discovery science
  • Neuroscience (ja)
  • Parkinson's disease
  • Resource
  • Science

ASJC Scopus subject areas

  • Health Informatics

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