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


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.

Publication DOI:
Divisions: 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 (, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Uncontrolled Keywords: Statistics and Probability,Biochemistry,Molecular Biology,Computer Science Applications,Computational Theory and Mathematics,Computational Mathematics
Publication ISSN: 1367-4803
Last Modified: 24 Apr 2024 07:18
Date Deposited: 19 Jul 2021 13:51
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Related URLs: https://academi ... btab536/6325084 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2021-12-15
Published Online Date: 2021-07-21
Accepted Date: 2021-07-19
Authors: Ashford, Jodie
Reis-Cunha, João
Lobo, Igor
Lobo, Francisco
Campelo, Felipe (ORCID Profile 0000-0001-8432-4325)



Version: Accepted Version

Access Restriction: Restricted to Repository staff only


Version: Published Version

License: Creative Commons Attribution

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