ArticleScientific reports2021
PCA-based unsupervised feature extraction for gene expression analysis of COVID-19 patients.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
- A systematic review of artificial intelligence-based COVID-19 modeling on multimodal genetic information.Progress in biophysics and molecular biology · 2023Pooled it
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- Uncloaking the black-box: the need for explainable artificial intelligence in clinical microbiology and infectious diseases applications.Frontiers in public health · 2026Review
- Transcriptomic Signatures of Monocyte-Derived Macrophages Associate With Androgen Receptor Pathway Inhibitor Progression in Prostate Cancer.The Prostate · 2025Article
- Skin-Interfaced Wearable Sweat Sensors for Precision Medicine.Chemical reviews · 2023Review
- Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment.Sensors (Basel, Switzerland) · 2023Review
- Gene network inference from single-cell omics data and domain knowledge for constructing COVID-19-specificFrontiers in genetics · 2023Article
- A Data Science Approach for the Identification of Molecular Signatures of Aggressive Cancers.Cancers · 2022Article
- PCA-based sub-surface structure and defect analysis for germanium-on-nothing using nanoscale surface topography.Scientific reports · 2022Article
- Article
- Exploratory Analysis of Sleep Deprivation Effects on Gene Expression and Regional Brain Metabolism.Complex psychiatryArticle
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronavirus disease 2019 (COVID-19) is raging worldwide. This potentially fatal infectious disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the complete mechanism of COVID-19 is not well understood. Therefore, we analyzed gene expression profiles of COVID-19 patients to identify disease-related genes through an innovative machine learning method that enables a data-driven strategy for gene selection from a data set with a small number of samples and many candidates. Principal-component-analysis-based unsupervised feature extraction (PCAUFE) was applied to the RNA expression profiles of 16 COVID-19 patients and 18 healthy control subjects. The results identified 123 genes as critical for COVID-19 progression from 60,683 candidate probes, including immune-related genes. The 123 genes were enriched in binding sites for transcription factors NFKB1 and RELA, which are involved in various biological phenomena such as immune response and cell survival: the primary mediator of canonical nuclear factor-kappa B (NF-κB) activity is the heterodimer RelA-p50. The genes were also enriched in histone modification H3K36me3, and they largely overlapped the target genes of NFKB1 and RELA. We found that the overlapping genes were downregulated in COVID-19 patients. These results suggest that canonical NF-κB activity was suppressed by H3K36me3 in COVID-19 patient blood.
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