A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity

Antigen immunogenicity and the specificity of binding of T-cell receptors to antigens are key properties underlying effective immune responses. Here we propose diffRBM, an approach based on transfer learning and Restricted Boltzmann Machines, to build sequence-based predictive models of these proper...

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Veröffentlicht in:	eLife Jg. 12
Hauptverfasser:	Bravi, Barbara, Di Gioacchino, Andrea, Fernandez-de-Cossio-Diaz, Jorge, Walczak, Aleksandra M, Mora, Thierry, Cocco, Simona, Monasson, Rémi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cambridge eLife Sciences Publications Ltd 08.09.2023 eLife Sciences Publication eLife Sciences Publications, Ltd
Schlagworte:	Antigens Cancer Computational and Systems Biology Datasets Immune response Immunogenicity Life Sciences Lymphocytes T machine learning Pathogens Peptides Prediction models Receptor mechanisms T cell receptors Transfer learning
ISSN:	2050-084X, 2050-084X
Online-Zugang:	Volltext
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