Estimating the Limits of Organism-Specific Training for Epitope Prediction

Research output: Chapter in Book/Published conference outputConference publication


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.
Original languageEnglish
Title of host publication4th Workshop on Artificial Intelligence and Big Data vs. Pandemics
Subtitle of host publicationat 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
ISBN (Electronic)979-8-3503-3748-8
Publication statusPublished - 1 Jan 2024
Event2023 IEEE International Conference on Bioinformatics and Biomedicine - Istanbul, Turkey
Duration: 5 Dec 20238 Dec 2023


Conference2023 IEEE International Conference on Bioinformatics and Biomedicine
Abbreviated titleBIBM 2023

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