RBM-MHC: A Semi-Supervised Machine-Learning Method for Sample-Specific Prediction of Antigen Presentation by HLA-I Alleles

The recent increase of immunopeptidomics data, obtained by mass spectrometry or binding assays, opens up possibilities for investigating endogenous antigen presentation by the highly polymorphic human leukocyte antigen class I (HLA-I) protein. State-of-the-art methods predict with high accuracy pres...

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Veröffentlicht in:	Cell systems Jg. 12; H. 2; S. 195
Hauptverfasser:	Bravi, Barbara, Tubiana, Jérôme, Cocco, Simona, Monasson, Rémi, Mora, Thierry, Walczak, Aleksandra M
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States 17.02.2021
Schlagworte:	Algorithms Alleles Antigen Presentation - genetics Antigen Presentation - immunology Computational Biology - methods Databases, Protein Epitopes Histocompatibility Antigens Class I - genetics Histocompatibility Antigens Class I - immunology HLA Antigens - genetics HLA Antigens - immunology Humans Machine Learning Major Histocompatibility Complex - immunology Mass Spectrometry - methods Models, Theoretical Peptides - chemistry Protein Binding SARS-CoV-2 epitopes antigen presentation Restricted Boltzmann Machines machine learning HLA prediction immunopeptidomics neoantigen discovery
ISSN:	2405-4720, 2405-4720
Online-Zugang:	Weitere Angaben
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