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

Jodie Ashford, João Reis-Cunha, Igor Lobo, Francisco Lobo, Felipe Campelo*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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 datasets 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 taxonspecific models may become a feasible alternative, with unexplored potential gains in predictive performance. Results: This article 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.

Original languageEnglish
Pages (from-to)4826–4834
Number of pages9
JournalBioinformatics
Volume37
Issue number24
Early online date21 Jul 2021
DOIs
Publication statusPublished - 15 Dec 2021

Bibliographical note

© 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.

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