Lessons Learned in the Analysis of High-dimensional Data in Vaccinomics

Overview

Journal Vaccine

Specialty Allergy & Immunology

Date 2015 May 10

PMID 25957070

Citations 17

Authors

Ann L Oberg

Brett A McKinney

Daniel J Schaid

V Shane Pankratz

Richard B Kennedy

Gregory A Poland

Affiliations

Soon will be listed here.

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.

Citing Articles

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Current Challenges in Vaccinology.

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PMID: 32670279 PMC: 7329983. DOI: 10.3389/fimmu.2020.01181.

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Current perspectives in assessing humoral immunity after measles vaccination.

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Perturbation of B Cell Gene Expression Persists in HIV-Infected Children Despite Effective Antiretroviral Therapy and Predicts H1N1 Response.

Cotugno N, de Armas L, Pallikkuth S, Rinaldi S, Issac B, Cagigi A Front Immunol. 2017; 8:1083.

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