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URL: https://github.com/XSLiuLab/Pep2TCR
Proper Citation: Pep2TCR (RRID:SCR_024721)
Description: Web tool for CD4 TCR specificity prediction and biology applications. Used for accurate prediction of CD4 T cell receptor binding specificity through transfer learning and ensemble approach. Repository comprises codes of Pep2TCR, providing comprehensive guidance for researchers interested in Pep2TCR.
Resource Type: data access protocol, software resource, source code, web service
Keywords: CD4 TCR specificity prediction, deep learning, transfer learning, ensemble learning, neoantigen, CD4+ T cell
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The NIDDK Information Network (dkNET) serves the needs of basic and clinical investigators by providing seamless access to large pools of data and research resources relevant to the mission of The National Institute of Diabetes Digestive and Kidney Diseases (NIDDK). dkNET is hosted at University of California San Diego, and is supported by NIH NIDDK grant U24DK097771.
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