Probing T-cell response by sequence-based probabilistic modeling

With the increasing ability to use high-throughput next-generation sequencing to quantify the diversity of the human T cell receptor (TCR) repertoire, the ability to use TCR sequences to infer antigen-specificity could greatly aid potential diagnostics and therapeutics. Here, we use a machine-learni...

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Veröffentlicht in:	PLoS computational biology Jg. 17; H. 9; S. e1009297
Hauptverfasser:	Bravi, Barbara, Balachandran, Vinod P., Greenbaum, Benjamin D., Walczak, Aleksandra M., Mora, Thierry, Monasson, Rémi, Cocco, Simona
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 02.09.2021 PLOS Public Library of Science (PLoS)
Schlagworte:	Amino acids Antigen (tumor-associated) Antigens Biology and Life Sciences Cancer Cancer Survivors Carcinoma, Pancreatic Ductal - immunology Cloning Cluster Analysis Computational Biology - methods Cytokines Datasets Datasets as Topic Epitopes Humans Immunotherapy Infectious diseases Information processing Learning algorithms Learning models (Stochastic processes) Life Sciences Lymphocytes Lymphocytes T Machine learning Mathematical models Medicine and Health Sciences Models, Statistical Mutation Neoantigens Next-generation sequencing Pancreatic Neoplasms - immunology Patients Peptides Physiological aspects Probabilistic models Receptors, Antigen, T-Cell - immunology Research and Analysis Methods T cell receptors T cells T-cell receptor T-Lymphocytes - immunology Tumors France
ISSN:	1553-7358, 1553-734X, 1553-7358
Online-Zugang:	Volltext
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