Some experiences and opportunities for big data in translational research

Christopher G. Chute, Mollie Ullman-Cullere, Grant M. Wood, Simon M. Lin, Min He, Jyotishman Pathak

Research output: Contribution to journalReview articlepeer-review

55 Scopus citations


Health care has become increasingly information intensive. The advent of genomic data, integrated into patient care, significantly accelerates the complexity and amount of clinical data. Translational research in the present day increasingly embraces new biomedical discovery in this data-intensive world, thus entering the domain of "big data." The Electronic Medical Records and Genomics consortium has taught us many lessons, while simultaneously advances in commodity computing methods enable the academic community to affordably manage and process big data. Although great promise can emerge from the adoption of big data methods and philosophy, the heterogeneity and complexity of clinical data, in particular, pose additional challenges for big data inferencing and clinical application. However, the ultimate comparability and consistency of heterogeneous clinical information sources can be enhanced by existing and emerging data standards, which promise to bring order to clinical data chaos. Meaningful Use data standards in particular have already simplified the task of identifying clinical phenotyping patterns in electronic health records.

Original languageEnglish (US)
Pages (from-to)802-809
Number of pages8
JournalGenetics in Medicine
Issue number10
StatePublished - Oct 2013


  • big data
  • clinical data representation
  • genomics
  • health information technology standards

ASJC Scopus subject areas

  • Genetics(clinical)


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