Selected article for: "antigen presentation and peptide presentation"

Author: Bravi, Barbara; Tubiana, Jérôme; Cocco, Simona; Monasson, Rémi; Mora, Thierry; Walczak, Aleksandra M.
Title: Flexible machine learning prediction of antigen presentation for rare and common HLA-I alleles
  • Cord-id: h71imtzl
  • Document date: 2020_9_19
  • ID: h71imtzl
    Snippet: The recent increase of immunopeptidomic data, obtained by mass spectrometry or binding assays, opens unprecedented possibilities for investigating endogenous antigen presentation by the highly polymorphic human leukocyte antigen class I (HLA-I) protein. We introduce a flexible and easily interpretable peptide presentation prediction method, RBM-MHC. We validate its performance as a predictor of cancer neoantigens and viral epitopes and we use it to reconstruct peptide motifs presented on specifi
    Document: The recent increase of immunopeptidomic data, obtained by mass spectrometry or binding assays, opens unprecedented possibilities for investigating endogenous antigen presentation by the highly polymorphic human leukocyte antigen class I (HLA-I) protein. We introduce a flexible and easily interpretable peptide presentation prediction method, RBM-MHC. We validate its performance as a predictor of cancer neoantigens and viral epitopes and we use it to reconstruct peptide motifs presented on specific HLA-I molecules. By benchmarking RBM-MHC performance on a wide range of HLA-I alleles, we show its importance to improve prediction accuracy for rarer alleles.

    Search related documents:
    Co phrase search for related documents
    • adam optimizer and machine learning: 1, 2
    • adaptive immune system and log likelihood: 1
    • adaptive immune system and loss function: 1
    • adaptive immune system and low dimensional: 1
    • adaptive immune system and low dimensional representation: 1
    • adaptive immune system and machine learning: 1, 2, 3, 4, 5
    • additional challenge and machine learning: 1, 2
    • loss function and low dimensional: 1
    • loss function and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23