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Improving MHC class I antigen-processing predictions using representation learning and cleavage site-specific kernels
In this work, we propose a new deep-learning model, MHCrank, to predict the probability that a peptide will be processed for presentation by MHC class I molecules. We find that the performance of our model is significantly higher than that of two previously published baseline methods: MHCflurry and...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Elsevier
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9499997/ https://www.ncbi.nlm.nih.gov/pubmed/36160050 http://dx.doi.org/10.1016/j.crmeth.2022.100293 |