Campelo, Felipe and Lobo, Francisco P. (2024). The rise of taxon-specific epitope predictors. Briefings in Bioinformatics, 25 (2),
Abstract
Computational predictors of immunogenic peptides, or epitopes, are traditionally built based on data from a broad range of pathogens without consideration for taxonomic information. While this approach may be reasonable if one aims to develop one-size-fits-all models, it may be counterproductive if the proteins for which the model is expected to generalize are known to come from a specific subset of phylogenetically-related pathogens. There is mounting evidence that, for these cases, taxon-specific models can outperform generalist ones, even when trained with substantially smaller amounts of data. In this comment we provide some perspective on the current state of taxon-specific modelling for the prediction of linear B-cell epitopes, and the challenges faced when building and deploying these predictors.
Publication DOI: | https://doi.org/10.1093/bib/bbae092 |
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Divisions: | College of Engineering & Physical Sciences > School of Computer Science and Digital Technologies > Applied AI & Robotics College of Engineering & Physical Sciences > Aston Centre for Artifical Intelligence Research and Application |
Additional Information: | Copyright © The Author(s) 2024. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
Uncontrolled Keywords: | data mining,epitope prediction,machine learning,phylogeny-aware modelling,Information Systems,Molecular Biology |
Publication ISSN: | 1477-4054 |
Last Modified: | 30 Sep 2024 13:41 |
Date Deposited: | 19 Mar 2024 16:38 |
Full Text Link: | |
Related URLs: |
http://www.scop ... tnerID=8YFLogxK
(Scopus URL) |
PURE Output Type: | Article |
Published Date: | 2024-03-16 |
Published Online Date: | 2024-03-16 |
Accepted Date: | 2024-02-18 |
Authors: |
Campelo, Felipe
(
0000-0001-8432-4325)
Lobo, Francisco P. |