{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Li Y"],"funding":["NCATS NIH HHS","NLM NIH HHS","National Institutes of Health"],"pagination":["648"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9030792"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["13(4)"],"pubmed_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"],"journal":["Genes"],"pubmed_title":["Using an Unsupervised Clustering Model to Detect the Early Spread of SARS-CoV-2 Worldwide."],"pmcid":["PMC9030792"],"funding_grant_id":["R01 LM013337","UL1 TR001422","1R01LM013337","5UL1TR001422"],"pubmed_authors":["Li Y","Zeng Z","Liu Q","Luo Y"],"additional_accession":[]},"is_claimable":false,"name":"Using an Unsupervised Clustering Model to Detect the Early Spread of SARS-CoV-2 Worldwide.","description":"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","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Apr","modification":"2026-05-19T03:11:53.664Z","creation":"2025-02-19T05:05:12.029Z"},"accession":"S-EPMC9030792","cross_references":{"pubmed":["35456454"],"doi":["10.3390/genes13040648"]}}