NeoTImmuML: a machine learning-based prediction model for human tumor neoantigen immunogenicity
TumorAgDB2.0 provides a comprehensive data resource for neoantigen research, while NeoTImmuML offers an efficient and interpretable tool for predicting neoantigen immunogenicity. Together, they offer valuable support for the design of personalized neoantigen vaccines and the development of cancer immunotherapy strategies.
Authors
- Shuguang Ge (ORCID: https://orcid.org/0000-0002-3583-2742)
- Qin Chu (ORCID: https://orcid.org/0000-0002-2434-0611)
- Ruizhe Dong
- Ying Shao (ORCID: https://orcid.org/0000-0002-0390-3315)
- Pengbo Wen (ORCID: https://orcid.org/0000-0002-3017-0137)
- Wei Ji (ORCID: https://orcid.org/0000-0001-8460-9312)
Institutions
- Xuzhou Medical College (CN)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2025-10-22
- DOI
- https://doi.org/10.3389/fimmu.2025.1681396
- Citations
- 1
- Primary Topic
- vaccines and immunoinformatics approaches
- Type
- article
- Field-Weighted Citation Impact
- 0.81
Funders
- National Natural Science Foundation of China
- Government of Jiangsu Province
- Xuzhou Medical University