BERTMHC: improved MHC–peptide class II interaction prediction with transformer and multiple instance learning
Author:
Affiliation:
1. NEC Laboratories Europe GmbH Kurfuersten-Anlage 36, 69115 Heidelberg, Germany
2. Transgene, Boulevard Gonthier d’Andernach, 67400 Illkirch-Graffenstaden, France
Abstract
Publisher
Oxford University Press (OUP)
Subject
Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability
Link
http://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btab422/38831512/btab422.pdf
Reference38 articles.
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2. NNAlign_MA: MHC peptidome deconvolution for accurate MHC binding motif characterization and improved T-cell epitope predictions;Alvarez;Mol. Cell. Proteomics,2019
3. Unsupervised HLA peptidome deconvolution improves ligand prediction accuracy and predicts cooperative effects in peptide–HLA interactions;Bassani-Sternberg;J. Immunol,2016
4. Predicting HLA class II antigen presentation through integrated deep learning;Chen;Nat. Biotechnol,2019
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