Bioinform. 2023 ,24, bbad086. [CrossRef] 88. Zhang, P .; Bang, S.; Cai, M.; Lee, H. Context-Aware Amino Acid Embedding Advances Analysis of TCR-Epitope Interactions. eLife 2023 ,12, RP88837. [CrossRef] 89. Pham, M.-D.N.; Nguyen, T.-N.; Tran, L.S.; Nguyen, Q.-T.B.; Nguyen, T.-P .H.; Pham, T.M.Q.; Nguyen, H.-N.; Giang, H.; Phan, M.-D.; Nguyen, V . epiTCR: A Highly Sensitive Predictor for TCR-Peptide Binding. Bioinformatics 2023 ,39, btad284. [CrossRef] 90. Croce, G.; Bobisse, S.; Moreno, D.L.; Schmidt, J.; Guillame, P .; Harari, A.; Gfeller, D. Deep Learning Predictions of TCR-Epitope Interactions Reveal Epitope-Specific Chains in Dual Alpha T Cells. bioRxiv 2023 . [CrossRef] 91. Korpela, D.; Jokinen, E.; Dumitrescu, A.; Huuhtanen, J.; Mustjoki, S.; Lähdesmäki, H. EPIC-TRACE: Predicting TCR Binding to Unseen Epitopes Using Attention and Contextualized Embeddings. Bioinformatics 2023 ,39, btad743. [CrossRef] [PubMed] 92. Bradley, P . Structure-Based Prediction of T Cell Receptor:Peptide-MHC Interactions. eLife 2023 ,12, e82813. [CrossRef] [PubMed] 93. Xu, H.; Hu, R.; Dong, X.; Suo, S.; Zhao, Z. Prediction and Characterization of T Cell Response by Improved T Cell Receptors to Antigen Specificity with Interpretable Deep Learning. bioRxiv 2023 .