ArticleComputers in biology and medicine2021
UMAP-assisted K-means clustering of large-scale SARS-CoV-2 mutation datasets.
Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
31 citing papers in PubMed.
- Three Data-Driven Lip Soft-Tissue Phenotypes and Their Dento-Skeletal Associations in Orthodontic Patients: An Exploratory Unsupervised Learning Study.Orthodontics & craniofacial research · 2026Article
- Machine Learning-Integrated Metabolomics for Precision Pharmacotherapy: Advances, Challenges, and Clinical Translation.Metabolites · 2026Review
- Cell painting and thermal proteome profiling for inference of drug targets and mechanism of action.Molecular systems biology · 2026Article
- Explainable Mortality Prediction for Liver Transplant Candidates with Hepatocellular Carcinoma: A Supervised Clustering Approach.Health data science · 2026Article
- Axon collateralization and focal myelin dystrophy alter action potential propagation in multicompartment pyramidal neuron models.PLoS computational biology · 2025Article
- [Creation of Patient Profiles and a Screening Tool for Individuals with Pain].Farmaceuticos comunitarios · 2025Article
- PREDAC-FluB: predicting antigenic clusters of seasonal influenza B viruses with protein language model embedding based convolutional neural network.Briefings in bioinformatics · 2025Article
- Evolution of AI enabled healthcare systems using textual data with a pretrained BERT deep learning model.Scientific reports · 2025Article
- Immune dysregulation in COVID-19 induced ARDS in kidney transplant recipients revealed by single-cell RNA sequencing.Scientific reports · 2025Article
- Personal identification using a cross-sectional hyperspectral image of a hand.Journal of biomedical optics · 2025Article
- Dietary patterns and obesity are associated with type 2 diabetes risk in elderly Chinese men: a machine learning approach.Frontiers in nutrition · 2025Article
- Refining SARS-CoV-2 intra-host variation by leveraging large-scale sequencing data.NAR genomics and bioinformatics · 2024Article
- Dietary patterns associated with the incidence of hypertension among adult Japanese males: application of machine learning to a cohort study.European journal of nutrition · 2024Article
- The role of strategic visibility in shaping wayfinding behavior in multilevel buildings.Scientific reports · 2024Article
- STW-MD: a novel spatio-temporal weighting and multi-step decision tree method for considering spatial heterogeneity in brain gene expression data.Briefings in bioinformatics · 2024Article
- Algorithm-Based Risk Identification in Patients with Breast Cancer-Related Lymphedema: A Cross-Sectional Study.Cancers · 2023Article
- Article
- Functional immunophenotyping of children with critical status asthmaticus identifies differential gene expression responses in neutrophils exposed to a poly(I:C) stimulus.Scientific reports · 2022Observational
- Review
- Article
Corrections and comments
- Update of
Authors and funding
4 authors.
Funding
Abstract
Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a worldwide devastating effect. Understanding the evolution and transmission of SARS-CoV-2 is of paramount importance for controlling, combating and preventing COVID-19. Due to the rapid growth in both the number of SARS-CoV-2 genome sequences and the number of unique mutations, the phylogenetic analysis of SARS-CoV-2 genome isolates faces an emergent large-data challenge. We introduce a dimension-reduced K-means clustering strategy to tackle this challenge. We examine the performance and effectiveness of three dimension-reduction algorithms: principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP). By using four benchmark datasets, we found that UMAP is the best-suited technique due to its stable, reliable, and efficient performance, its ability to improve clustering accuracy, especially for large Jaccard distanced-based datasets, and its superior clustering visualization. The UMAP-assisted K-means clustering enables us to shed light on increasingly large datasets from SARS-CoV-2 genome isolates.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.