More details
→ Library based fully-supervised prediction: This module uses data from Keskin et al. to make predictions.
→ Mono-allelic prediction: This module is used to predict peptides presented to a single HLA allele.
→ Semi-supervised Patient Prediction: This module is used to predict peptides presented by any HLA allele in a patient.
→ Use to obtain Insights of presented peptides
See owner's GitHub repository for more information: https://github.com/lshh125/epiNB-website
Liang, S., Jiang, X., Chiu, Y., Xu, H., Kim, K. H., Lizee, G., & Chen, K. (2023). An interpretable ML model to characterize patient-specific HLA-I antigen presentation. bioRxiv : the preprint server for biology, 2023.03.12.532264. https://doi.org/10.1101/2023.03.12.532264
Training data for the purposed of library-based prediction are curated from: http://hlathena.tools/.
This application was not uploaded by the author, but through their publicly available Github repository, https://github.com/lshh125/epiNB-website.
© 2022 Ken Chen lab. All rights reserved.
Warning: Not intended for clinical use. Assume outputs are unsafe and unvalidated. Use carefully.
- Precision Medicine & Genomics