Prediction of Malaria Vaccination Outcomes from Gene Expression Data

Ahmad Shayaan, Indu Ilanchezian, Shrisha Rao

2019

Abstract

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.

Paper Citation

in Harvard Style

Shayaan A., Ilanchezian I. and Rao S. (2019). Prediction of Malaria Vaccination Outcomes from Gene Expression Data. In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - Volume 3: BIOINFORMATICS; ISBN 978-989-758-353-7, SciTePress, pages 155-162. DOI: 10.5220/0007260501550162

in Bibtex Style

@conference{bioinformatics19,
author={Ahmad Shayaan and Indu Ilanchezian and Shrisha Rao},
title={Prediction of Malaria Vaccination Outcomes from Gene Expression Data},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - Volume 3: BIOINFORMATICS},
year={2019},
pages={155-162},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007260501550162},
isbn={978-989-758-353-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - Volume 3: BIOINFORMATICS
TI - Prediction of Malaria Vaccination Outcomes from Gene Expression Data
SN - 978-989-758-353-7
AU - Shayaan A.
AU - Ilanchezian I.
AU - Rao S.
PY - 2019
SP - 155
EP - 162
DO - 10.5220/0007260501550162
PB - SciTePress