ArticlePloS one2022
Multi-omic analysis reveals enriched pathways associated with COVID-19 and COVID-19 severity.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 25 citations in OpenAlex.
- Scalable randomized kernel methods for multiview data integration and prediction with application to Coronavirus disease.Biostatistics (Oxford, England) · 2025Article
- Enhanced Production of Lipid Mediators in Plasma and Activation of DNA Damage Pathways in PBMCs Are Correlated With the Severity of Ancestral SARS-CoV-2 Infection.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2025Article
- MixOmics Integration of Biological Datasets Identifies Highly Correlated Variables of COVID-19 Severity.International journal of molecular sciences · 2025Article
- Using prior-data conflict to tune Bayesian regularized regression models.Statistics and computing · 2025Article
- Extracting a COVID-19 signature from a multi-omic dataset.Frontiers in bioinformatics · 2025Article
- Proteomic analysis of lung responses to SARS-CoV-2 infection in aged non-human primates: clinical and research relevance.GeroScience · 2024Article
- SARS-CoV-2 rapidly evolves lineage-specific phenotypic differences when passaged repeatedly in immune-naïve mice.Communications biology · 2024Article
- mvlearnR and Shiny App for multiview learning.Bioinformatics advances · 2024Article
- COVID-19-Omics Report: From Individual Omics Approaches to Precision Medicine.Reports (MDPI) · 2023Review
- Article
- Integrative multi-omics approach for identifying molecular signatures and pathways and deriving and validating molecular scores for COVID-19 severity and status.BMC genomics · 2023Article
- Chewing the fat: How lipidomics is changing our understanding of human health and disease in 2022.Analytical science advances · 2023Review
- Pregnancy-specific responses to COVID-19 revealed by high-throughput proteomics of human plasma.Communications medicine · 2023Article
- Serum proteomics hint at an early T-cell response and modulation of SARS-CoV-2-related pathogenic pathways in COVID-19-ARDS treated with Ruxolitinib.Frontiers in medicine · 2023Article
- Pregnancy-specific responses to COVID-19 are revealed by high-throughput proteomics of human plasma.Research square · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
COVID-19 is a disease characterized by its seemingly unpredictable clinical outcomes. In order to better understand the molecular signature of the disease, a recent multi-omics study was done which looked at correlations between biomolecules and used a tree- based machine learning approach to predict clinical outcomes. This study specifically looked at patients admitted to the hospital experiencing COVID-19 or COVID-19 like symptoms. In this paper we examine the same multi-omics data, however we take a different approach, and we identify stable molecules of interest for further pathway analysis. We used stability selection, regularized regression models, enrichment analysis, and principal components analysis on proteomics, metabolomics, lipidomics, and RNA sequencing data, and we determined key molecules and biological pathways in disease severity, and disease status. In addition to the individual omics analyses, we perform the integrative method Sparse Multiple Canonical Correlation Analysis to analyse relationships of the different view of data. Our findings suggest that COVID-19 status is associated with the cell cycle and death, as well as the inflammatory response. This relationship is reflected in all four sets of molecules analyzed. We further observe that the metabolic processes, particularly processes to do with vitamin absorption and cholesterol are implicated in COVID-19 status and severity.
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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.