A sequential-structural hybrid method for linear B-cell epitope prediction

Baijiang Wang

doi:10.1117/12.2669539

24 March 2023 A sequential-structural hybrid method for linear B-cell epitope prediction

Baijiang Wang

Proceedings Volume 12611, Second International Conference on Biological Engineering and Medical Science (ICBioMed 2022); 126112K (2023) https://doi.org/10.1117/12.2669539
Event: International Conference on Biological Engineering and Medical Science (ICBioMed2022), 2022, Oxford, United Kingdom

Abstract

Linear B-cell epitope prediction, as an in scilio evaluation tool in many immunological applications, has gained much attention in recent years. As epitope regions do not have a clear boundary, its prediction is generally difficult and inaccurate. The author proposes a hybrid model SSEPred that combines peptide sequence embeddings, multiple propensity scales and XGBoost, and conducts detailed research on the effects of peptide embeddings and propensity scales. The final model yields ROC AUC of 71.6% and F1-score of 80.9% by five-fold crossvalidation on reduced IEDB linear B-cell epitope dataset, which outperforms most existing methods.

Citation Download Citation

Baijiang Wang "A sequential-structural hybrid method for linear B-cell epitope prediction", Proc. SPIE 12611, Second International Conference on Biological Engineering and Medical Science (ICBioMed 2022), 126112K (24 March 2023); https://doi.org/10.1117/12.2669539

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