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CoVnita, an end-to-end privacy-preserving framework for SARS-CoV-2 classification
Classification of viral strains is essential in monitoring and managing the COVID-19 pandemic, but patient privacy and data security concerns often limit the extent of the open sharing of full viral genome sequencing data. We propose a framework called CoVnita, that supports private training of a cl...
| Autores principales: | , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10166033/ https://www.ncbi.nlm.nih.gov/pubmed/37156790 http://dx.doi.org/10.1038/s41598-023-34535-8 |