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...

Full description

Saved in:

Bibliographic Details
Published in:	Cell systems Vol. 12; no. 2; p. 195
Main Authors:	Bravi, Barbara, Tubiana, Jérôme, Cocco, Simona, Monasson, Rémi, Mora, Thierry, Walczak, Aleksandra M
Format:	Journal Article
Language:	English
Published:	United States 17.02.2021
Subjects:	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 Access:	Get more information
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!