Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/27156
Title: | Uncertainty Estimation in SARS-CoV-2 B-Cell Epitope Prediction for Vaccine Development |
Authors: | Ghoshal, B Ghoshal, B Swift, S Tucker, A |
Keywords: | Covid-19;vaccine development;dropweights;epitope prediction;deep learning;uncertainty estimation;B-cell epitopes |
Issue Date: | 22-May-2021 |
Publisher: | Springer Nature |
Citation: | Ghoshal, B. et al. (2021) 'Uncertainty Estimation in SARS-CoV-2 B-Cell Epitope Prediction for Vaccine Development', Proceedings of the Artificial Intelligence in Medicine 19th International Conference on Artificial Intelligence in Medicine, AIME 2021, Virtual Event, 15–18 june (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, Vol 12721 LNAI). pp. 361 - 366. doi: 10.1007/978-3-030-77211-6_41. |
Series/Report no.: | Lecture Notes in Computer Science book series;volume 12721 Lecture Notes in Artificial Intelligence (LNAI); |
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 to combat Covid-19. |
Description: | Paper accepted for the 19th International Conference on Artificial Intelligence in Medicine. Important: e-prints posted on arXiv are not peer-reviewed by arXiv; they should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the field. The article on this institutional repository is a preprint. It may not have been certified by peer review. |
URI: | https://bura.brunel.ac.uk/handle/2438/27156 |
DOI: | https://doi.org/10.1007/978-3-030-77211-6_41 |
ISBN: | 978-3-030-77210-9 (pbk) 978-3-030-77211-6 (ebk) |
ISSN: | 0302-9743 |
Other Identifiers: | ORCID iDs: Stephen Swift https://orcid.org/0000-0001-8918-3365; Allan Tucker https://orcid.org/0000-0001-5105-3506. |
Appears in Collections: | Dept of Computer Science Research Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Preprint.pdf | Copyright © The Authors 2021) This is a paper accepted for the 19th International Conference on Artificial Intelligence in Medicine, made available on arXiv under a Creative Commons (CC -0) Attributtion License (https://creativecommons.org/publicdomain/zero/1.0/). | 400.1 kB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License