Lessons learned in the analysis of high-dimensional data in vaccinomics

Ann L. Oberg, Brett A. McKinney, Daniel J. Schaid, V. Shane Pankratz, Richard B. Kennedy, Gregory A. Poland

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


The field of vaccinology is increasingly moving toward the generation, analysis, and modeling of extremely large and complex high-dimensional datasets. We have used data such as these in the development and advancement of the field of vaccinomics to enable prediction of vaccine responses and to develop new vaccine candidates. However, the application of systems biology to what has been termed "big data," or "high-dimensional data," is not without significant challenges-chief among them a paucity of gold standard analysis and modeling paradigms with which to interpret the data. In this article, we relate some of the lessons we have learned over the last decade of working with high-dimensional, high-throughput data as applied to the field of vaccinomics. The value of such efforts, however, is ultimately to better understand the immune mechanisms by which protective and non-protective responses to vaccines are generated, and to use this information to support a personalized vaccinology approach in creating better, and safer, vaccines for the public health.

Original languageEnglish (US)
Pages (from-to)5262-5270
Number of pages9
Issue number40
StatePublished - Sep 29 2015


  • Data interpretation, statistical
  • Immunogenetics
  • Systems biology
  • Vaccination
  • Vaccines

ASJC Scopus subject areas

  • Molecular Medicine
  • Immunology and Microbiology(all)
  • veterinary(all)
  • Public Health, Environmental and Occupational Health
  • Infectious Diseases


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