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• W2894849296 abstract "Predicting epitopes recognized by cytotoxic T cells has been a long standing challenge within the field of immuno- and bioinformatics. While reliable predictions of peptide binding are available for most Major Histocompatibility Complex class I (MHCI) alleles, prediction models of T cell receptor (TCR) interactions with MHC class I-peptide complexes remain poor due to the limited amount of available training data. Recent next generation sequencing projects have however generated a considerable amount of data relating TCR sequences with their cognate HLA-peptide complex target. Here, we utilize such data to train a sequence-based predictor of the interaction between TCRs and peptides presented by the most common human MHCI allele, HLA-A*02:01. Our model is based on convolutional neural networks, which are especially designed to meet the challenges posed by the large length variations of TCRs. We show that such a sequence-based model allows for the identification of TCRs binding a given cognate peptide-MHC target out of a large pool of non-binding TCRs." @default.
• W2894849296 created "2018-10-12" @default.
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• W2894849296 date "2018-10-03" @default.
• W2894849296 modified "2023-10-16" @default.
• W2894849296 title "NetTCR: sequence-based prediction of TCR binding to peptide-MHC complexes using convolutional neural networks" @default.
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• W2894849296 doi "https://doi.org/10.1101/433706" @default.
• W2894849296 hasPublicationYear "2018" @default.
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