BeeTLe: A Framework for Linear B-Cell Epitope Prediction and Classification-Reference-Cited by-同舟云学术

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BeeTLe: A Framework for Linear B-Cell Epitope Prediction and Classification

Published:2023 Issue: Volume: Page:477-494
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
Short-container-title:

Author:

Yuan Xiao

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-43427-3_29

Reference54 articles.

1. Ashford, J., Reis-Cunha, J., Lobo, I., Lobo, F., Campelo, F.: Organism-specific training improves performance of linear B-cell epitope prediction. Bioinformatics 37(24), 4826–4834 (2021). https://doi.org/10.1093/bioinformatics/btab536

2. Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)

3. Bahai, A., Asgari, E., Mofrad, M.R., Kloetgen, A., McHardy, A.C.: EpitopeVec: linear epitope prediction using deep protein sequence embeddings. Bioinformatics 37(23), 4517–4525 (2021). https://doi.org/10.1093/bioinformatics/btab467

4. Chen, T., Guestrin, C.: XGBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016). https://doi.org/10.1145/2939672.2939785

5. Cheng, J., Dong, L., Lapata, M.: Long short-term memory-networks for machine reading. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 551–561 (2016). https://doi.org/10.18653/v1/D16-1053

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