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lshh125/epiNB-website
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Efficient, precise and interpretable HLA-I binding epitope identification based on Naive Bayes formulation

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Use Cases Limitations Evidence Owner's Insight

→ 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.

Prototype

Warning: Not intended for clinical use. Assume outputs are unsafe and unvalidated. Use carefully.


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