Uncertainty Estimation in SARS-CoV-2 B-cell Epitope Prediction for Vaccine Development
Abstract
B-cell epitopes play a key role in stimulating B-cells, triggering the primary immune response which results in antibody production as well as the establishment of long-term immunity in the form of memory cells. Consequently, being able to accurately predict appropriate linear B-cell epitope regions would pave the way for the development of new protein-based vaccines. Knowing how much confidence there is in a prediction is also essential for gaining clinicians' trust in the technology. In this article, we propose a calibrated uncertainty estimation in deep learning to approximate variational Bayesian inference using MC-DropWeights to predict epitope regions using the data from the immune epitope database. Having applied this onto SARS-CoV-2, it can more reliably predict B-cell epitopes than standard methods. This will be able to identify safe and effective vaccine candidates against Covid-19.
Keywords
Cite
@article{arxiv.2103.11214,
title = {Uncertainty Estimation in SARS-CoV-2 B-cell Epitope Prediction for Vaccine Development},
author = {Bhargab Ghoshal and Biraja Ghoshal and Stephen Swift and Allan Tucker},
journal= {arXiv preprint arXiv:2103.11214},
year = {2021}
}
Comments
Paper accepted for the 19th International Conference on Artificial Intelligence in Medicine