ArticleGenes2021
Investigating Cellular Trajectories in the Severity of COVID-19 and Their Transcriptional Programs Using Machine Learning Approaches.
Article in Genes, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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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.
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Who cites it
13 citing papers in PubMed, 17 citations in OpenAlex.
- Host-directed antiviral strategy targeting prohibitins: Mel56 suppresses influenza A virus and severe acute respiratory syndrome coronavirus 2 via modulation of antioxidant pathways and mitochondrial function.Microbiology spectrum · 2026Article
- Single-Cell RNA-Seq Profiling of Transposable Element Expression in Human Peripheral Blood Cells During Viral Infections.International journal of molecular sciences · 2026Article
- Machine learning and deep learning to identifying subarachnoid haemorrhage macrophage-associated biomarkers by bulk and single-cell sequencing.Journal of cellular and molecular medicine · 2024Article
- EP300 as a Molecular Integrator of Fibrotic Transcriptional Programs.International journal of molecular sciences · 2023Review
- Peripheral Blood Genes Crosstalk between COVID-19 and Sepsis.International journal of molecular sciences · 2023Article
- SARS-CoV-2 Covid-19 Infection During Pregnancy and Differential DNA Methylation in Human Cord Blood Cells From Term Neonates.Epigenetics insights · 2023Article
- Machine Learning and COVID-19: Lessons from SARS-CoV-2.Advances in experimental medicine and biology · 2023Article
- Article
- Delineating COVID-19 immunological features using single-cell RNA sequencing.Innovation (Cambridge (Mass.)) · 2022Review
- Macrophage Boolean networks in the time of SARS-CoV-2.Frontiers in immunology · 2022Article
- Cell-Type-Specific Profibrotic Scores across Multi-Organ Systems Predict Cancer Prognosis.Cancers · 2021Article
- Co-Regulation of Protein Coding Genes by Transcription Factor and Long Non-Coding RNA in SARS-CoV-2 Infected Cells: An In Silico Analysis.Non-coding RNA · 2021Article
- Association of CXCR6 with COVID-19 severity: delineating the host genetic factors in transcriptomic regulation.Human genetics · 2021Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Single-cell RNA sequencing of the bronchoalveolar lavage fluid (BALF) samples from COVID-19 patients has enabled us to examine gene expression changes of human tissue in response to the SARS-CoV-2 virus infection. However, the underlying mechanisms of COVID-19 pathogenesis at single-cell resolution, its transcriptional drivers, and dynamics require further investigation. In this study, we applied machine learning algorithms to infer the trajectories of cellular changes and identify their transcriptional programs. Our study generated cellular trajectories that show the COVID-19 pathogenesis of healthy-to-moderate and healthy-to-severe on macrophages and T cells, and we observed more diverse trajectories in macrophages compared to T cells. Furthermore, our deep-learning algorithm DrivAER identified several pathways (e.g., xenobiotic pathway and complement pathway) and transcription factors (e.g., MITF and GATA3) that could be potential drivers of the transcriptomic changes for COVID-19 pathogenesis and the markers of the COVID-19 severity. Moreover, macrophages-related functions corresponded more to the disease severity compared to T cells-related functions. Our findings more proficiently dissected the transcriptomic changes leading to the severity of a COVID-19 infection.
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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.