| Autor: |
Xue Zhang, Jingcheng Wu, Joseph Baeza, Katie Gu, Zhan Zhou |
| Rok vydání: |
2023 |
| DOI: |
10.1101/2023.02.13.528393 |
| Popis: |
The transport of antigenic peptides from cytoplasm to the endoplasmic reticulum (ER) via transporter associated with antigen processing (TAP) is a critical step during the presentation of tumor neoantigens. The application of computational approaches significantly speed up the analysis of this biological process. Here, we present a tool named DeepTAP for TAP-binding peptide prediction, which employs a sequence-based multilayered recurrent neural network (RNN). Compared with traditional machine learning and other available prediction tools, DeepTAP achieves state-of-the-art performance on the benchmark datasets. The source code and dataset of DeepTAP are available freely via GitHub athttps://github.com/zjupgx/DeepTAP. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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