Unsupervised clustering of SARS-CoV-2 using deep convolutional autoencoder

Unsupervised clustering of SARS-CoV-2 using deep convolutional autoencoder

Abstract SARS-CoV-2’s population structure might have a substantial impact on public health management and diagnostics if it can be identified. It is critical to rapidly monitor and characterize their lineages circulating globally for a more accurate diagnosis, improved care, and faster treatment. F...

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Bibliographic Details
Main Authors: Fayroz F. Sherif, Khaled S. Ahmed
Format: Article
Language:English
Published: SpringerOpen 2022-08-01
Series:Journal of Engineering and Applied Science
Subjects:
SARS-CoV-2
Unsupervised clustering
Deep learning
Convolutional autoencoder
Spike protein
Lineages
Online Access:https://doi.org/10.1186/s44147-022-00125-0

Internet

https://doi.org/10.1186/s44147-022-00125-0

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