EpicPred: predicting phenotypes driven by epitope-binding TCRs using attention-based multiple instance learning

Motivation Correctly identifying epitope-binding T-cell receptors (TCRs) is important to both understand their underlying biological mechanism in association to some phenotype and accordingly develop T-cell mediated immunotherapy treatments. Although the importance of the CDR3 region in TCRs for epi...

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Bibliographic Details
Published in:	Bioinformatics (Oxford, England) Vol. 41; no. 3
Main Authors:	Jeon, Jaemin, Yu, Suwan, Lee, Sangam, Kim, Sang Cheol, Jo, Hye-Yeong, Jung, Inuk, Kim, Kwangsoo
Format:	Journal Article
Language:	English
Published:	England Oxford University Press 04.03.2025
Subjects:	Computational Biology - methods COVID-19 - immunology COVID-19 - virology Epitopes - immunology Epitopes, T-Lymphocyte - immunology Epitopes, T-Lymphocyte - metabolism Humans Machine Learning Multiple-Instance Learning Algorithms Neoplasms - immunology Original Paper Phenotype Receptors, Antigen, T-Cell - chemistry Receptors, Antigen, T-Cell - genetics Receptors, Antigen, T-Cell - immunology Receptors, Antigen, T-Cell - metabolism SARS-CoV-2 - immunology Software
ISSN:	1367-4811, 1367-4803, 1367-4811
Online Access:	Get full text
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