Journal Article Epublish 8 citations
PubMed: 36902333 DOI: 10.3390/ijms24054905
Classification of COVID-19 Patients into Clinically Relevant Subsets by a Novel Machine Learning Pipeline Using Transcriptomic Features.
Int J Mol Sci 24(5)
Authors: Andrea R Daamen, Prathyusha Bachali, Amrie C Grammer, Peter E Lipsky
Affiliations
- AMPEL BioSolutions LLC, Charlottesville, VA 22902, USA.
- RILITE Research Institute, Charlottesville, VA 22902, USA.
Abstract
The persistent impact of the COVID-19 pandemic and heterogeneity in disease manifestations point to a need for innovative approaches to identify drivers of immune pathology and predict whether infected patients will present with mild/moderate or severe disease. We have developed a novel iterative machine learning pipeline that utilizes gene enrichment profiles from blood transcriptome data to stratify COVID-19 patients based on disease severity and differentiate severe COVID cases from other patients with acute hypoxic respiratory failure. The pattern of gene module enrichment in COVID-19 patients overall reflected broad cellular expansion and metabolic dysfunction, whereas increased neutrophils, activated B cells, T-cell lymphopenia, and proinflammatory cytokine production were specific to severe COVID patients. Using this pipeline, we also identified small blood gene signatures indicative of COVID-19 diagnosis and severity that could be used as biomarker panels in the clinical setting.
MeSH
Cells, Genes, Blood, Classification, Machine Learning, Covid-19, T-Lymphocytes, B-Lymphocytes
Cited by
- Progress of the "Molecular Informatics" Section in 2022. Int J Mol Sci, 2023