PBMC transcriptomes after influenza vaccination contain valuable information about factors affecting vaccine responses. However, distilling meaningful knowledge out of these complex datasets is often difficult and requires advanced data mining algorithms. We investigated the use of the data-drivenWorldwide, influenza affects 5-10% of adults annually, and results in an estimated 250,000 to 500,000 deaths 1 . Influenza morbidity and influenza-associated deaths increase significantly with age 2,3 , and more than 90% of influenza-associated deaths occur in individuals ≥65 years of age 4 . Although seasonal influenza vaccination offers protection against severe influenza disease, levels of protection vary between seasons, individuals, and agetending to be lower in elderly populations [5][6][7][8][9][10][11][12] . In fact, the effectiveness of seasonal trivalent inactivated influenza vaccination among community-dwelling older adults has been estimated to be only 30-40% 6,[12][13][14] . With the aging of populations in the U.S. and globally, it is imperative that influenza vaccine-induced immunity in older adults be better understood [15][16][17] . Systems vaccinology and vaccinomics, the application of systems biology to the study of vaccines, are a promising method to better understand human immune responses to vaccines from a holistic perspective 18,19 . A seminal paper by Querec et al. in 2008 applied a systems biology approach to study yellow fever vaccine-induced immunity and identified novel genes involved in vaccine-induced antibody and CD8+ T cell responses whose expression levels could predict immunogenicity of the vaccine in subjects, setting a new standard for vaccine studies 20 . Such systems biology approaches provide complementary insights to reductionist approaches by revealing novel interactions between immune system processes critical to developing immune responses to vaccines 21 . Systems biology approaches have been previously applied to the study of seasonal influenza vaccination