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 journalArticle

18 Citations (Scopus)

Abstract

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)
JournalVaccine
DOIs
StateAccepted/In press - 2015

Fingerprint

Vaccines
vaccines
vaccine development
Systems Biology
gold
public health
Public Health
Biological Sciences
prediction

Keywords

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

ASJC Scopus subject areas

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

Cite this

Lessons learned in the analysis of high-dimensional data in vaccinomics. / Oberg, Ann L; McKinney, Brett A.; Schaid, Daniel J; Pankratz, V. Shane; Kennedy, Richard B; Poland, Gregory A.

In: Vaccine, 2015.

Research output: Contribution to journalArticle

@article{5a53a91b8fdf4cf9bbc3e754593f6a08,
title = "Lessons learned in the analysis of high-dimensional data in vaccinomics",
abstract = "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.",
keywords = "Data interpretation, statistical, Immunogenetics, Systems biology, Vaccination, Vaccines",
author = "Oberg, {Ann L} and McKinney, {Brett A.} and Schaid, {Daniel J} and Pankratz, {V. Shane} and Kennedy, {Richard B} and Poland, {Gregory A.}",
year = "2015",
doi = "10.1016/j.vaccine.2015.04.088",
language = "English (US)",
journal = "Vaccine",
issn = "0264-410X",
publisher = "Elsevier BV",

}

TY - JOUR

T1 - Lessons learned in the analysis of high-dimensional data in vaccinomics

AU - Oberg, Ann L

AU - McKinney, Brett A.

AU - Schaid, Daniel J

AU - Pankratz, V. Shane

AU - Kennedy, Richard B

AU - Poland, Gregory A.

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

KW - Data interpretation, statistical

KW - Immunogenetics

KW - Systems biology

KW - Vaccination

KW - Vaccines

UR - http://www.scopus.com/inward/record.url?scp=84929493746&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84929493746&partnerID=8YFLogxK

U2 - 10.1016/j.vaccine.2015.04.088

DO - 10.1016/j.vaccine.2015.04.088

M3 - Article

JO - Vaccine

JF - Vaccine

SN - 0264-410X

ER -