On this episode I discuss the use of AI-based approaches for the development and discovery of effective vaccines.
Black, Steve et al. “Transforming vaccine development.” Seminars in immunology vol. 50 (2020): 101413. doi:10.1016/j.smim.2020.101413
Ong, Edison et al. “COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning.” Frontiers in immunology vol. 11 1581. 3 Jul. 2020, doi:10.3389/fimmu.2020.01581
Tomic, Adriana et al. “SIMON, an Automated Machine Learning System, Reveals Immune Signatures of Influenza Vaccine Responses.” Journal of immunology (Baltimore, Md. : 1950) vol. 203,3 (2019): 749-759. doi:10.4049/jimmunol.1900033
Moxon, Richard et al. “Editorial: Reverse Vaccinology.” Frontiers in immunology vol. 10 2776. 3 Dec. 2019, doi:10.3389/fimmu.2019.02776
He, Yongqun et al. “Vaxign: the first web-based vaccine design program for reverse vaccinology and applications for vaccine development.” Journal of biomedicine & biotechnology vol. 2010 (2010): 297505. doi:10.1155/2010/297505
Ong, Edison et al. “Vaxign-ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens.” Bioinformatics (Oxford, England) vol. 36,10 (2020): 3185-3191. doi:10.1093/bioinformatics/btaa119
Yang, Brian et al. “Protegen: a web-based protective antigen database and analysis system.” Nucleic acids research vol. 39,Database issue (2011): D1073-8. doi:10.1093/nar/gkq944
Yang, Z., Bogdan, P. & Nazarian, S. An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study. Sci Rep 11, 3238 (2021). https://doi.org/10.1038/s41598-021-81749-9
Tomar, Namrata, and Rajat K De. “Immunoinformatics: an integrated scenario.” Immunology vol. 131,2 (2010): 153-68. doi:10.1111/j.1365-2567.2010.03330.x
Keshavarzi Arshadi, Arash et al. “Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development.” Frontiers in artificial intelligence vol. 3 65. 18 Aug. 2020, doi:10.3389/frai.2020.00065
Wu, Jingcheng et al. “DeepHLApan: A Deep Learning Approach for Neoantigen Prediction Considering Both HLA-Peptide Binding and Immunogenicity.” Frontiers in immunology vol. 10 2559. 1 Nov. 2019, doi:10.3389/fimmu.2019.02559