Estimating the Limits of Organism-Specific Training for Epitope Prediction

Abstract

The identification of linear B-cell epitopes is an important task in the development of vaccines, therapeutic antibodies and several diagnostic tests. Recently, organism-specific training has been shown to improve prediction performance for data-rich organisms. This article investigates the limits of organism-specific training for epitope prediction, by systematically quantifying the effect of the amount of training data on the performance of the models developed. The results obtained indicate that even models trained on small organism-specific data sets can outperform similar models trained on much larger heterogeneous and mixed data sets, as well as widely-used predictors from the literature, which are trained on heterogeneous data. These results suggest the potential for a much broader applicability of pathogen-specific models, which can be used to accelerate the development of diagnostic tests and vaccines in the context of emerging pathogens and to support faster responses in future disease outbreaks.

Divisions: College of Engineering & Physical Sciences > School of Computer Science and Digital Technologies > Applied AI & Robotics
College of Engineering & Physical Sciences > Engineering for Health
College of Engineering & Physical Sciences > School of Computer Science and Digital Technologies
College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Aston Centre for Artifical Intelligence Research and Application
Funding Information: J.A was supported by the Engineering and Physical Sciences Research Council (EPSRC DTP grant EP/R512989/1). Experiments were run using Aston EPS Machine Learning Server (EPSRC Core Equipment Fund, Grant EP/V036106/1)
Additional Information: Copyright © 2023, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Event Title: 2023 IEEE International Conference on Bioinformatics and Biomedicine
Event Type: Other
Event Dates: 2023-12-05 - 2023-12-08
ISBN: 979-8-3503-3748-8
Last Modified: 13 May 2024 07:39
Date Deposited: 05 Dec 2023 18:16
Full Text Link:
Related URLs: https://www.com ... 381/1TOb88MKze0 (Publisher URL)
PURE Output Type: Conference contribution
Published Date: 2024-01-01
Accepted Date: 2023-11-10
Authors: Ashford, Jodie
Ekárt, Anikó (ORCID Profile 0000-0001-6967-5397)
Campelo, Felipe (ORCID Profile 0000-0001-8432-4325)

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