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 human migration factors shaped the specific geographical distribution of the population structure. This study provides a different approach using clustering methods to study the population structure of a never-seen-before and fast-growing species such as SARS-CoV-2. Moreover, clustering techniques can be used for further studies of local population structures of the proliferating virus.

Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis

“…Accompanied by the development of single-cell-based [177] and spatial-based [178] technologies that have been applied in molecular studies, numerous DL models are becoming more popular for computationally intensive analysis. To deal with the complexity of large genomics data, unsupervised deep clustering tools have been built for population structure identification [179] or cell population subtype annotation [180] , [181] , [182] , [183] . In addition, to process the complex structure of multi-omics data, graph neural network (GNN) models are increasingly popular in dataset integration [184] , biomedical classification [185] , prognosis prediction [186] , and so on.…”