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


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
Issue number24
Early online date21 Jul 2021
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 (, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.


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