ArticleInternational journal of molecular sciences2022
Combining Deep Phenotyping of Serum Proteomics and Clinical Data via Machine Learning for COVID-19 Biomarker Discovery.
Article in International journal of molecular sciences, 2022. 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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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Progress and trends on machine learning in proteomics during 1997-2024: a bibliometric analysis.Frontiers in medicine · 2025Pooled it
- Integrated miR-omics and proteomics reveal the regulatory role of miR in protein networks associated with COVID-19 disease progression.Frontiers in immunology · 2026Article
- Research Trends in Proteomic Studies Using Serum from COVID-19 Patients: A Bibliometric Analysis.Current medicinal chemistry · 2025Article
- The Impact of Serum/Plasma Proteomics on SARS-CoV-2 Diagnosis and Prognosis.International journal of molecular sciences · 2024Review
- Machine Learning Identifies Key Proteins in Primary Sclerosing Cholangitis Progression and Links High CCL24 to Cirrhosis.International journal of molecular sciences · 2024Article
- Behind the mask: a critical perspective on the ethical, moral, and legal implications of AI in ophthalmology.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Review
- Complex changes in serum protein levels in COVID-19 convalescents.Scientific reports · 2024Article
- Investigating the relationship between the immune response and the severity of COVID-19: a large-cohort retrospective study.Frontiers in immunology · 2024Observational
- Extracellular vesicle features are associated with COVID-19 severity.Journal of cellular and molecular medicine · 2023Article
- Benchmarking of Machine Learning classifiers on plasma proteomic for COVID-19 severity prediction through interpretable artificial intelligence.Artificial intelligence in medicine · 2023Review
- Recent Progress in Research on COVID-19 Pathophysiology: Biomarkers, Repurposed Drugs, Viral Invasiveness, SARS-CoV-2 Genetic Diversity, the Crystal Structure of Viral Proteins, and the Molecular and Cellular Outcomes of COVID-19.International journal of molecular sciences · 2022Article
Corrections and comments
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Authors and funding
19 authors at 2 institutions in 1 country.
Funding
Abstract
The persistence of long-term coronavirus-induced disease 2019 (COVID-19) sequelae demands better insights into its natural history. Therefore, it is crucial to discover the biomarkers of disease outcome to improve clinical practice. In this study, 160 COVID-19 patients were enrolled, of whom 80 had a "non-severe" and 80 had a "severe" outcome. Sera were analyzed by proximity extension assay (PEA) to assess 274 unique proteins associated with inflammation, cardiometabolic, and neurologic diseases. The main clinical and hematochemical data associated with disease outcome were grouped with serological data to form a dataset for the supervised machine learning techniques. We identified nine proteins (i.e., CD200R1, MCP1, MCP3, IL6, LTBP2, MATN3, TRANCE, α2-MRAP, and KIT) that contributed to the correct classification of COVID-19 disease severity when combined with relative neutrophil and lymphocyte counts. By analyzing PEA, clinical and hematochemical data with statistical methods that were able to handle many variables in the presence of a relatively small sample size, we identified nine potential serum biomarkers of a "severe" outcome. Most of these were confirmed by literature data. Importantly, we found three biomarkers associated with central nervous system pathologies and protective factors, which were downregulated in the most severe cases.
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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.