MATHLA: a robust framework for HLA-peptide binding prediction integrating bidirectional LSTM and multiple head attention mechanism

BACKGROUND: Accurate prediction of binding between class I human leukocyte antigen (HLA) and neoepitope is critical for target identification within personalized T-cell based immunotherapy. Many recent prediction tools developed upon the deep learning algorithms and mass spectrometry data have indee...

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Detalles Bibliográficos
Autores principales: Ye, Yilin, Wang, Jian, Xu, Yunwan, Wang, Yi, Pan, Youdong, Song, Qi, Liu, Xing, Wan, Ji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7787246/
https://www.ncbi.nlm.nih.gov/pubmed/33407098
http://dx.doi.org/10.1186/s12859-020-03946-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7787246/
https://www.ncbi.nlm.nih.gov/pubmed/33407098
http://dx.doi.org/10.1186/s12859-020-03946-z

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