Organism-Specific Training Improves Performance of Linear B-Cell Epitope Prediction

Abstract

Motivation: In silico identification of linear B-cell epitopes represents an important step in the development of diagnostic tests and vaccine candidates, by providing potential high-probability targets for experimental investigation. Current predictive tools were developed under a generalist approach, training models with heterogeneous data sets to develop predictors that can be deployed for a wide variety of pathogens. However, continuous advances in processing power and the increasing amount of epitope data for a broad range of pathogens indicate that training organism or taxon-specific models may become a feasible alternative, with unexplored potential gains in predictive performance. Results: This paper shows how organism-specific training of epitope prediction models can yield substantial performance gains across several quality metrics when compared to models trained with heterogeneous and hybrid data, and with a variety of widely-used predictors from the literature. These results suggest a promising alternative for the development of custom-tailored predictive models with high predictive power, which can be easily implemented and deployed for the investigation of specific pathogens.

Publication DOI: https://doi.org/10.1093/bioinformatics/btab536
Divisions: College of Engineering & Physical Sciences > Computer Science
College of Engineering & Physical Sciences
Additional Information: © The Author(s) 2021. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Full Text Link:
Related URLs: https://academi ... btab536/6325084 (Publisher URL)
PURE Output Type: Article
Published Date: 2021-08-11
Published Online Date: 2021-08-11
Accepted Date: 2021-07-19
Authors: Ashford, Jodie
Reis-Cunha, João
Lobo, Igor
Lobo, Francisco
Campelo, Felipe (ORCID Profile 0000-0001-8432-4325)

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