Using an Unsupervised Clustering Model to Detect the Early Spread of SARS-CoV-2 Worldwide.

ABSTRACT: Deciphering the population structure of SARS-CoV-2 is critical to inform public health management and reduce the risk of future dissemination. With the continuous accruing of SARS-CoV-2 genomes worldwide, discovering an effective way to group these genomes is critical for organizing the landscape of the population structure of the virus. Taking advantage of recently published state-of-the-art machine learning algorithms, we used an unsupervised deep learning clustering algorithm to group a total of 16,873 SARS-CoV-2 genomes. Using single nucleotide polymorphisms as input features, we identified six major subtypes of SARS-CoV-2. The proportions of the clusters across the continents revealed distinct geographical distributions. Comprehensive analysis indicated that both genetic factors and h

SUBMITTER: Li Y 

PROVIDER: S-EPMC9030792 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature