Uncertainty Estimation in SARS-CoV-2 B-cell Epitope Prediction for Vaccine Development

• URL: http://arxiv.org/abs/2103.11214v1
• Date: Sat, 20 Mar 2021 17:10:49 GMT
• Title: Uncertainty Estimation in SARS-CoV-2 B-cell Epitope Prediction for Vaccine Development
• Authors: Bhargab Ghoshal, Biraja Ghoshal, Stephen Swift, Allan Tucker
• Abstract summary: Knowing how much confidence there is in a prediction is also essential for gaining clinicians' trust in the technology. Knowing how much confidence there is in a prediction is also essential for gaining clinicians' trust in the technology.
• Score: 0.36130723421895944
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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.

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