TEINet: a deep learning framework for prediction of TCR-epitope binding specificity

Yuepeng Jiang; Miaozhe Huo; Shuai Cheng Li

doi:10.1101/2022.10.20.513029

TEINet: a deep learning framework for prediction of TCR-epitope binding specificity

Yuepeng Jiang, Miaozhe Huo, Shuai Cheng Li

2022 · doi:10.1101/2022.10.20.513029

preprint OA: closed CC-BY-NC-4.0

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References (24)

Attention-aware contrastive learning for predicting T cell receptor-antigen binding specificity via crossref
Deep autoregressive generative models capture the intrinsics embedded in T-cell receptor repertoires via crossref
Interpretable deep learning to uncover the molecular binding patterns determining TCR–epitope interactions via crossref
NetTCR: sequence-based prediction of TCR binding to peptide-MHC complexes using convolutional neural networks via crossref
Prediction of specific TCR-peptide binding from large dictionaries of TCR-peptide pairs via crossref
doi:10.3389/fimmu.2019.02820 via crossref
doi:10.1371/journal.pcbi.1008814 via crossref
doi:10.1093/bib/bbaa318 via crossref
doi:10.1126/sciadv.abf5835 via crossref
doi:10.1038/s42256-021-00383-2 via crossref
doi:10.1038/nature22383 via crossref
doi:10.1159/000445656 via crossref
doi:10.3389/fimmu.2019.02080 via crossref
doi:10.1038/ng.3822 via crossref
doi:10.1093/bib/bbaa415 via crossref
doi:10.1107/s0907444998009378 via crossref
doi:10.1073/pnas.160252097 via crossref
doi:10.1093/bioinformatics/btz614 via crossref
doi:10.1038/nbt.4282 via crossref
doi:10.1073/pnas.1500973112 via crossref
doi:10.1126/science.274.5284.94 via crossref
doi:10.1016/j.cell.2019.07.009 via crossref
doi:10.1093/nar/gky1006 via crossref
doi:10.1093/bioinformatics/btx286 via crossref

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License: CC-BY-NC-4.0

[1] Attention-aware contrastive learning for predicting T cell receptor-antigen binding specificity via crossref

[2] Deep autoregressive generative models capture the intrinsics embedded in T-cell receptor repertoires via crossref

[3] Interpretable deep learning to uncover the molecular binding patterns determining TCR–epitope interactions via crossref

[4] NetTCR: sequence-based prediction of TCR binding to peptide-MHC complexes using convolutional neural networks via crossref

[5] Prediction of specific TCR-peptide binding from large dictionaries of TCR-peptide pairs via crossref

[6] doi:10.3389/fimmu.2019.02820 via crossref

[7] doi:10.1371/journal.pcbi.1008814 via crossref

[8] doi:10.1093/bib/bbaa318 via crossref

[9] doi:10.1126/sciadv.abf5835 via crossref

[10] doi:10.1038/s42256-021-00383-2 via crossref

[11] doi:10.1038/nature22383 via crossref

[12] doi:10.1159/000445656 via crossref

[13] doi:10.3389/fimmu.2019.02080 via crossref

[14] doi:10.1038/ng.3822 via crossref

[15] doi:10.1093/bib/bbaa415 via crossref

[16] doi:10.1107/s0907444998009378 via crossref

[17] doi:10.1073/pnas.160252097 via crossref

[18] doi:10.1093/bioinformatics/btz614 via crossref

[19] doi:10.1038/nbt.4282 via crossref

[20] doi:10.1073/pnas.1500973112 via crossref

[21] doi:10.1126/science.274.5284.94 via crossref

[22] doi:10.1016/j.cell.2019.07.009 via crossref

[23] doi:10.1093/nar/gky1006 via crossref

[24] doi:10.1093/bioinformatics/btx286 via crossref