Abstract
B-cell epitopes play a key role in stimulating B-cells, triggering the primary immune response which results in antibody production as well as the establishment of long-term immunity in the form of memory cells. Consequently, being able to accurately predict appropriate linear B-cell epitope regions would pave the way for the development of new protein-based vaccines. Knowing how much confidence there is in a prediction is also essential for gaining clinicians’ trust in the technology. In this article, we propose a calibrated uncertainty estimation in deep learning to approximate variational Bayesian inference using MC-DropWeights to predict epitope regions using the data from the immune epitope database. Having applied this onto SARS-CoV-2, it can more reliably predict B-cell epitopes than standard methods. This will be able to identify safe and effective vaccine candidates to combat Covid-19.
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References
Janeway, C.A., Travers, P., Walport, M.: Immunobiology: the immune system in health and disease. Garland Publishing Inc. (1999)
Sanchez-Trincado, J.L., Gomez-Perosanz, M., Reche, P.A.: Fundamentals and methods for t-and b-cell epitope prediction. J. Immunol. Res. 2017 (2017)
Hopp, T.P., Woods, K.R.: Prediction of protein antigenic determinants from amino acid sequences. Proc. Natl. Acad. Sci. 78(6), 3824–3828 (1981)
Hopp, T.P., Woods, K.R.: A computer program for predicting protein antigenic determinants. Mol. Immunol. 20(4), 483–489 (1983)
Lins, L., Thomas, A., Brasseur, R.: Analysis of accessible surface of residues in proteins. Protein Sci. 12(7), 1406–1417 (2003)
Blythe, M.J., Flower, D.R.: Benchmarking b cell epitope prediction: underperformance of existing methods. Protein Sci. 14(1), 246–248 (2005)
Neal, R.M.: Bayesian learning via stochastic dynamics. In: Advances in Neural Information Processing Systems, pp. 475–482 (1993)
MacKay, D.J.: A practical Bayesian framework for backpropagation networks. Neural Comput. 4(3), 448–472 (1992)
Gal, Y.: Uncertainty in deep learning, PhD thesis, University of Cambridge (2016)
Kwon, Y., Won, J.-H., Kim, B.J., Paik, M.C.: Uncertainty quantification using Bayesian neural networks in classification: application to ischemic stroke lesion segmentation. Med. Imaging Deep Learn. Conf. (2018)
Blundell, C., Cornebise, J., Kavukcuoglu, K., Wierstra, D.: Weight uncertainty in neural networks, arXiv preprint arXiv:1505.05424 (2015)
Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. In: 33rd International Conference on Machine Learning, ICML 2016, vol. 3, pp. 1651–1660 (2016)
Ghoshal, B., Tucker, A., Sanghera, B., Lup Wong, W.: Estimating uncertainty in deep learning for reporting confidence to clinicians in medical image segmentation and diseases detection, Computational Intelligence (2019)
Ghoshal, B., Tucker, A.: Estimating uncertainty and interpretability in deep learning for coronavirus (covid-19) detection, arXiv preprint arXiv:2003.10769 (2020)
COVID-19/SARS B-cell Epitope Prediction (2019). https://www.kaggle.com/c/data-science-bowl2018
Ghoshal, B., Tucker, A.: On calibrated model uncertainty in deep learning. In: The European Conference on Machine Learning (ECML PKDD 2020) (2020)
Nomi, T., et al.: Epitope prediction of antigen protein using attention-based LSTM network, BioRxiv (2020)
Mak, T., Saunders, M., Jett, B.: B cell development, activation and effector functions, primer to the immune response. 2nd ed: Academic Cell, pp. 111–42 (2014)
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Ghoshal, B., Ghoshal, B., Swift, S., Tucker, A. (2021). Uncertainty Estimation in SARS-CoV-2 B-Cell Epitope Prediction for Vaccine Development. In: Tucker, A., Henriques Abreu, P., Cardoso, J., Pereira Rodrigues, P., Riaño, D. (eds) Artificial Intelligence in Medicine. AIME 2021. Lecture Notes in Computer Science(), vol 12721. Springer, Cham. https://doi.org/10.1007/978-3-030-77211-6_41
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