NeoaPred: a deep-learning framework for predicting immunogenic neoantigen based on surface and structural features of peptide–human leukocyte antigen complexes
The source code is released under an Apache v2.0 license and is available at the GitHub repository (https://github.com/Dulab2020/NeoaPred).
Authors
- Hongli Du (ORCID: https://orcid.org/0000-0002-6419-0160)
- Meiling Hu
- Zixi Chen (ORCID: https://orcid.org/0000-0001-6350-2997)
- Xiaoyun Lu (ORCID: https://orcid.org/0000-0001-7931-6873)
- Hongmin Cai (ORCID: https://orcid.org/0000-0002-2747-7234)
- Binbin Xi (ORCID: https://orcid.org/0000-0001-7290-7566)
- Dawei Jiang (ORCID: https://orcid.org/0000-0002-9123-8393)
- W.C. Tan
- Dong Chen (ORCID: https://orcid.org/0000-0003-1198-6058)
- Jinfen Wei (ORCID: https://orcid.org/0000-0002-0850-3775)
Institutions
- Jinan University (CN)
- Ministry of Education (TH)
- South China University of Technology (CN)
Publication Details
- Journal
- Bioinformatics
- Published
- 2024-09-01
- DOI
- https://doi.org/10.1093/bioinformatics/btae547
- Citations
- 16
- Primary Topic
- vaccines and immunoinformatics approaches
- Type
- article
- Field-Weighted Citation Impact
- 3.74