Abstract
Neoantigens are crucial in distinguishing cancer cells from normal ones and play a significant role in cancer immunotherapy. The field of bioinformatics prediction for tumor neoantigens has rapidly developed, focusing on the prediction of peptide-HLA binding affinity. In this chapter, we introduce a user-friendly tool named DeepHLApan, which utilizes deep learning techniques to predict neoantigens by considering both peptide-HLA binding affinity and immunogenicity. We provide the application of DeepHLApan, along with the source code, docker version, and web-server. These resources are freely available at https://github.com/zjupgx/deephlapan and http://pgx.zju.edu.cn/deephlapan/.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China [Grant No. 31971371], the Zhejiang Provincial Natural Sciences Foundation of China [Grant No. LQ24H300005], and the China Postdoctoral Science Foundation [Grant No. 2022M712778]. We thank the Information Technology Center, State Key Lab of CAD&CG, Zhejiang University, and Alibaba Cloud for the support of computing resources.
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Wu, J., Li, J., Chen, S., Zhou, Z. (2024). DeepHLApan: A Deep Learning Approach for the Prediction of Peptide-HLA Binding and Immunogenicity. In: Boegel, S. (eds) HLA Ty**. Methods in Molecular Biology, vol 2809. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3874-3_15
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DOI: https://doi.org/10.1007/978-1-0716-3874-3_15
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