Machine Learning for Predicting Vaccine Immunogenicity

Abstract

The ability to predict how different individuals will respond to vaccination and to understand what best protects individuals from infection greatly facilitates developing next-generation vaccines. It facilitates both the rapid design and evaluation of new and emerging vaccines and identifies individuals unlikely to be protected by vaccine. We describe a general-purpose machine-learning framework, DAMIP, for discovering gene signatures that can predict vaccine immunity and efficacy. DAMIP is a multiple-group, concurrent classifier that offers unique features not present in other models: a nonlinear data transformation to manage the curse of dimensionality and noise; a reserved-judgment region that handles fuzzy entities; and constraints on the allowed percentage of misclassifications. Using DAMIP, implemented results for yellow fever demonstrated that, for the first time, a vaccine’s ability to immunize a patient could be successfully predicted (with accuracy of greater than 90 percent) within one week after vaccination. A gene identified by DAMIP, EIF2AK4, decrypted a seven-decade-old mystery of vaccination. Results for flu vaccine demonstrated DAMIP’s applicability to both live-attenuated and inactivated vaccines. Results in a malaria study enabled targeted delivery to individual patients. Our project’s methods and findings permit highlighting and probabilistically prioritizing hypothesis design to enhance biological discovery. Moreover, they guide the rapid development of better vaccines to fight emerging infections, and improve monitoring for poor responses in the elderly, infants, or others with weakened immune systems. In addition, the project’s work should help with universal flu-vaccine design.

Models for predicting the evolution of influenza to inform vaccine strain selection

“…22 Current efforts to predict the evolution of influenza try to map the serology data onto genetic sequences and phylogenetic trees. 31,32,34,37 Occasionally, new strains, which are antigenically different from the circulating dominant strains, still fail to spread in the population although they have a higher probability of fixation. 31 Therefore, incorporating serology data to consider empirically informed associations between viral genotypes and their antigenic characteristics in addition to genetic mutations may improve the predictive power of statistical models in detecting emerging strains.…”

“…Similarly, Sun et al 10 utilized bootstrapped ridge regression and antigenic mapping to quantify antigenic difference between influenza strains based on the HA1 sequence data. Recently, Lee et al 37 developed a general purpose computational framework called DAMIP to discover gene signatures that can predict vaccine immunity and efficacy.…”

Infectious disease mRNA vaccines and a review on epitope prediction for vaccine design

“…Immunogenicity prediction is another aspect of vaccine design that involves heavy use of computational methods. Lee et al [ 38 ] proposed a novel machine learning framework that predicted vaccine and antibody responses by uncovering gene signatures. This framework integrated a combinatorial feature-selection algorithm and an optimisation-based classification model known as discriminant analysis through mixed-integer programming (DAMIP).…”

Antigenicity prediction and vaccine recommendation of human influenza virus A (H3N2) using convolutional neural networks

“…PSO 58,59 was designed to optimize the choice of amino acid indices, and the structure and the hyperparameters of our CNN model. Our PSO algorithm was implemented in Python 3.6 and worked as follows:…”