Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Investigating Cellular Trajectories in the Severity of COVID-19 and Their Transcriptional Programs Using Machine Learning Approaches

24 Apr 2021

Abstract excerpt

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...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to COVID-19 Clinical Research Studies
Share to Immune responses and vaccinations
Share to Single-cell and spatial transcriptomics

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 33923155
DOI: 10.3390/genes12050635

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Investigating Cellular Trajectories in the Severity of COVID-19 and Their Transcriptional Programs Using Machine Learning ApproachesPMID 33923155Read ↗

Discriminating mild from critical COVID-19 by innate and adaptive immune single-cell profiling of bronchoalveolar lavages.Semantic proximity Read ↗

Comparative transcriptomic analyses of peripheral blood mononuclear cells of patients with non-pneumonia and severe pneumonia at 1 year-Long-COVID-19Semantic proximity Read ↗

People critically ill with COVID-19 exhibit peripheral immune profiles predictive of mortality and reflective of SARS-CoV-2 lung viral burdenSemantic proximity Read ↗

Dissecting the common and compartment-specific features of COVID-19 severity in the lung and periphery with single-cell resolutionSemantic proximity Read ↗

Cellular and molecular heterogeneities and signatures, and pathological trajectories of fatal COVID-19 lungs defined by spatial single-cell transcriptome analysisSemantic proximity Read ↗

Deep Learning Analysis of Single-Cell Data Reveals Unique Genetic Features Of COVID-19 SeveritiesSemantic proximity Read ↗

Transcriptomics secondary analysis of severe human infection with SARS-CoV-2 identifies gene expression changes and predicts three transcriptional biomarkers in leukocytesSemantic proximity Read ↗

Molecular signatures in the progression of COVID-19 severitySemantic proximity Read ↗

Transcriptomic Similarities and Differences in Host Response between SARS-CoV-2 and Other Viral InfectionsSemantic proximity Read ↗

Uncovering the Pathophysiological Pattern of Expression from Integrated Analysis across Uniformly Processed RNA Sequencing COVID-19 DatasetsSemantic proximity Read ↗

Multiomic analysis reveals cell-type-specific molecular determinants of COVID-19 severity.Semantic proximity Read ↗

Comprehensive Transcriptomic Analysis of COVID-19 Blood, Lung, and AirwaySemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.