Idioma:

Unsupervised machine learning framework for discriminating major variants of concern during COVID-19

Chandra, Rohitash ; Bansal, Chaarvi ; Kang, Mingyue ; Blau, Tom ; Agarwal, Vinti ; Singh, Pranjal ; Wilson, Laurence O W ; Vasan, Seshadri Roy, Satyaki

PloS one, 2023-05, Vol.18 (5), p.e0285719-e0285719 [Periódico revisado por pares]

United States: Public Library of Science

Texto completo disponível

Citações Citado por

Enviar para

Título:
Unsupervised machine learning framework for discriminating major variants of concern during COVID-19
Autor: Chandra, Rohitash ; Bansal, Chaarvi ; Kang, Mingyue ; Blau, Tom ; Agarwal, Vinti ; Singh, Pranjal ; Wilson, Laurence O W ; Vasan, Seshadri
Roy, Satyaki
Assuntos: Acute respiratory distress syndrome ; Algorithms ; Bioinformatics ; Biology and Life Sciences ; Care and treatment ; Cluster analysis ; Clustering ; Computer and Information Sciences ; Coronaviruses ; COVID-19 ; COVID-19 - epidemiology ; COVID-19 - genetics ; COVID-19 vaccines ; Datasets ; Deoxyribonucleic acid ; Diagnosis ; Disease transmission ; DNA ; Embedding ; Forecasts and trends ; Gene mutations ; Gene sequencing ; Genomes ; Genomics ; Global economy ; Health aspects ; Humans ; Impact analysis ; Learning algorithms ; Machine learning ; Medicine and Health Sciences ; Methods ; Middle East respiratory syndrome ; Mortality ; Multiple organ failure ; Mutation ; Mutation rates ; Pandemics ; Physical Sciences ; Prevention ; Principal components analysis ; Reduction ; Research and Analysis Methods ; Risk factors ; SARS-CoV-2 - genetics ; Severe acute respiratory syndrome coronavirus 2 ; Stochasticity ; Unsupervised learning ; Unsupervised Machine Learning ; Viral diseases ; Viruses ; World economy
É parte de: PloS one, 2023-05, Vol.18 (5), p.e0285719-e0285719
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Competing Interests: The authors have declared that no competing interests exist.
Descrição: Due to the high mutation rate of the virus, the COVID-19 pandemic evolved rapidly. Certain variants of the virus, such as Delta and Omicron emerged with altered viral properties leading to severe transmission and death rates. These variants burdened the medical systems worldwide with a major impact to travel, productivity, and the world economy. Unsupervised machine learning methods have the ability to compress, characterize, and visualize unlabelled data. This paper presents a framework that utilizes unsupervised machine learning methods to discriminate and visualize the associations between major COVID-19 variants based on their genome sequences. These methods comprise a combination of selected dimensionality reduction and clustering techniques. The framework processes the RNA sequences by performing a k-mer analysis on the data and further visualises and compares the results using selected dimensionality reduction methods that include principal component analysis (PCA), t-distributed stochastic neighbour embedding (t-SNE), and uniform manifold approximation projection (UMAP). Our framework also employs agglomerative hierarchical clustering to visualize the mutational differences among major variants of concern and country-wise mutational differences for selected variants (Delta and Omicron) using dendrograms. We also provide country-wise mutational differences for selected variants via dendrograms. We find that the proposed framework can effectively distinguish between the major variants and has the potential to identify emerging variants in the future.
Editor: United States: Public Library of Science
Idioma: Inglês

Links

View this record in MEDLINE/PubMed

Voltar para lista de resultados

Realização: Logos de Redes Sociais:

Unsupervised machine learning framework for discriminating major variants of concern during COVID-19

Chandra, Rohitash ; Bansal, Chaarvi ; Kang, Mingyue ; Blau, Tom ; Agarwal, Vinti ; Singh, Pranjal ; Wilson, Laurence O W ; Vasan, Seshadri Roy, Satyaki

United States: Public Library of Science

Buscando em bases de dados remotas. Favor aguardar.