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...

Full description

Bibliographic Details
Main Authors:	Liu, X. (Author), Pan, Y. (Author), Song, Q. (Author), Wan, J. (Author), Wang, J. (Author), Wang, Y. (Author), Xu, Y. (Author), Ye, Y. (Author)
Format:	Article
Language:	English
Published:	BioMed Central Ltd 2021
Subjects:	algorithm Algorithms Attention mechanisms biology Cancer immunotherapy chemistry Computational Biology Cross-validation tests Deep learning Forecasting Genes Histocompatibility Antigens Class I HLA antigen class 1 HLA-peptide binding prediction human Human leukocyte antigen Humans Interaction pattern Learning algorithms Long short-term memory Mass spectrometry Mass spectrometry data metabolism Models, Statistical Neural Networks, Computer peptide Peptides Prediction accuracy Prediction performance procedures protein binding Protein Binding statistical model Statistical tests Target identification T-cells
Online Access:	View Fulltext in Publisher

Internet

View Fulltext in Publisher

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

Internet

Similar Items