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.
| Original language | English |
|---|---|
| Article number | bbae092 |
| Number of pages | 3 |
| Journal | Briefings in Bioinformatics |
| Volume | 25 |
| Issue number | 2 |
| Early online date | 16 Mar 2024 |
| DOIs | |
| Publication status | Published - 16 Mar 2024 |
Bibliographical note
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
Keywords
- data mining
- epitope prediction
- machine learning
- phylogeny-aware modelling