Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Dec 2;17(12):5573-5574.
doi: 10.1080/21645515.2021.1991711. Epub 2021 Dec 13.

Requirements of integrated computational approach for developing personalized cancer vaccines

Elham Maserat  1 , Mohaddeseh Nasiri Hooshmand  1
Affiliations

Requirements of integrated computational approach for developing personalized cancer vaccines

Elham Maserat et al. Hum Vaccin Immunother. .
No abstract available

Keywords: Vaccine; cancer; computational approach.

PubMed Disclaimer

Conflict of interest statement

No potential conflict of interest was reported by the author(s).

References

    1. Tran NH, Qiao R, Xin L, Chen X, Shan B, Li M.. Personalized deep learning of individual immunopeptidomes to identify neoantigens for cancer vaccines. Nature Mach Intell. 2020;2(12):764–71. doi:10.1038/s42256-020-00260-4. - DOI
    1. Chen B, Khodadoust MS, Olsson N, Wagar LE, Fast E, Liu CL, Muftuoglu Y, Sworder BJ, Diehn M, Levy R, et al. Predicting HLA class II antigen presentation through integrated deep learning. Nat Biotechnol. 2019;37(11):1332–43. doi:10.1038/s41587-019-0280-2 - DOI - PMC - PubMed
    1. Kiyotani K, Toyoshima Y, Nakamura Y.. Immunogenomics in personalized cancer treatments. J Hum Genet. 2021;1–7. - PubMed
    1. De Mattos-Arruda L, Vazquez M, Finotello F, Lepore R, Porta E, Hundal J, Amengual-Rigo P, Ng CKY, Valencia A, Carrillo J, et al. Neoantigen prediction and computational perspectives towards clinical benefit: recommendations from the ESMO precision medicine working group. Ann Oncol. 2020;31(8):978–90. doi:10.1016/j.annonc.2020.05.008. - DOI - PMC - PubMed
    1. Phloyphisut P, Pornputtapong N, Sriswasdi S, Chuangsuwanich E. MHCSeqNet: a deep neural network model for universal MHC binding prediction. BMC Bioinform. 2019;20(1):1–10. doi:10.1186/s12859-019-2892-4. - DOI - PMC - PubMed