2015 Wagner Prize Winner - GA Tech & CDC

Machine Learning Framework for Predicting Vaccine Immunogenicity

The ability to better predict how different individuals will respond to vaccination and to understand what best protects individuals from infection greatly facilitates developing next-generation vaccines. We present a general-purpose, machine-learning framework for discovering gene signatures that can predict vaccine immunity and efficacy. Our models offer unique features not found in other models simultaneously. We will describe the implemented results for yellow fever and influenza vaccines, and highlight their implications for public health and precision medicine.