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...

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Detalles Bibliográficos
Autores principales: Sim, Jun Jie, Zhou, Weizhuang, Chan, Fook Mun, Annamalai, Meenatchi Sundaram Muthu Selva, Deng, Xiaoxia, Tan, Benjamin Hong Meng, Aung, Khin Mi Mi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
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

Internet

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

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