Vaccine development is a laborious and time-consuming process and can
benefit from statistical machine learning techniques, which can produce
general outcomes based on the patterns observed in the limited available
empirical data. In this paper, we show how limited gene expression data
from a small sample of subjects can be used to predict the outcomes of
malaria vaccine. In addition, we also draw inferences from the gene
expression data, with over 22000 columns (or features), by visualizing
the data, and reduce the data dimensions based on this inference for
efficient model training. Our methods are general and reliable and can
be extended to vaccines developed against any pathogen. Given the gene
expression data from a sample of subjects administered with a novel
vaccine, our methods can be used to test the outcome of that vaccine,
without the need for empirical observations on a larger population. By
carefully tuning the available data and the machine learning models, we
are able to achieve greater than 98\% accuracy, with sensitivity and
specificity of 0.93 and 1 respectively, in predicting the outcomes of
the malaria vaccine in developing immunogenicity against the malaria
pathogen.