https://orcid.org/0000-0003-1359-8024
References
- 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.
- 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
- Kiyotani K, Toyoshima Y, Nakamura Y. Immunogenomics in personalized cancer treatments. J Hum Genet. 2021;1–7.
- 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.
- 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.
- Kaushik AC, Li M, Mehmood A, Dai X, Wei D-Q ACPS. An accurate bioinformatics tool for precision-based anti-cancer peptide generation via omics data. Chem Biol Drug Design. 2021;97(2):372–82. doi:10.1111/cbdd.13789.
- Yamankurt G, Berns EJ, Xue A, Lee A, Bagheri N, Mrksich M, Mirkin CA. Exploration of the nanomedicine-design space with high-throughput screening and machine learning. Nature Biomed Eng. 2019;3(4):318–27. doi:10.1038/s41551-019-0351-1.
- Cherryholmes GA, Stanton SE, Disis ML. Current methods of epitope identification for cancer vaccine design. Vaccine. 2015;33(51):7408–14. doi:10.1016/j.vaccine.2015.06.116.