Prediction of Malaria Vaccination Outcomes from Gene Expression Data

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.