Baseline gene signatures of reactogenicity to Ebola vaccination: a machine learning approach across multiple cohorts.

ABSTRACT:

Introduction

The rVSVDG-ZEBOV-GP (Ervebo®) vaccine is both immunogenic and protective against Ebola. However, the vaccine can cause a broad range of transient adverse reactions, from headache to arthritis. Identifying baseline reactogenicity signatures can advance personalized vaccinology and increase our understanding of the molecular factors associated with such adverse events.

Methods

In this study, we developed a machine learning approach to integrate prevaccination gene expression data with adverse events that occurred within 14 days post-vaccination.

Results and discussion

We analyzed the expression of 144 genes across 343 blood samples collected from participants of 4 phase I clinical trial cohorts: Switzerland, USA, Gabon, and Kenya. Our machine learning appro

SUBMITTER: Gonzalez Dias Carvalho PC 

PROVIDER: S-EPMC10663260 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature