ArticleBiomolecules2022
Identification of Transcriptome Biomarkers for Severe COVID-19 with Machine Learning Methods.
Article in Biomolecules, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 11 citations in OpenAlex.
- Article
- Comparative review of artificial intelligence for transcriptomic biomarker discovery in coronavirus disease 2019 (COVID-19).Briefings in bioinformatics · 2026Review
- Genetic determinants of metabolic-inflammatory dysregulation and machine learning prediction of COVID-19.Frontiers in cellular and infection microbiology · 2026Article
- TransGeneSelector: using a transformer approach to mine key genes from small transcriptomic datasets in plant responses to various environments.BMC genomics · 2025Article
- Identification of Gene Signatures Associated with COVID-19 across Children, Adolescents, and Adults in the Nasopharynx and Peripheral Blood by Using a Machine Learning Approach.Current gene therapy · 2025Article
- Diphenyl pyridine intervention improvesFrontiers in genetics · 2025Article
- Comprehensive analysis of immunogenic cell death-related gene and construction of prediction model based on WGCNA and multiple machine learning in severe COVID-19.Scientific reports · 2024Article
- Systems biology approaches to identify driver genes and drug combinations for treating COVID-19.Scientific reports · 2024Article
- Classification of COVID-19 Patients into Clinically Relevant Subsets by a Novel Machine Learning Pipeline Using Transcriptomic Features.International journal of molecular sciences · 2023Article
Corrections and comments
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Authors and funding
8 authors at 6 institutions in 1 country.
Funding
Abstract
The rapid spread of COVID-19 has become a major concern for people's lives and health all around the world. COVID-19 patients in various phases and severity require individualized treatment given that different patients may develop different symptoms. We employed machine learning methods to discover biomarkers that may accurately classify COVID-19 in various disease states and severities in this study. The blood gene expression profiles from 50 COVID-19 patients without intensive care, 50 COVID-19 patients with intensive care, 10 non-COVID-19 individuals without intensive care, and 16 non-COVID-19 individuals with intensive care were analyzed. Boruta was first used to remove irrelevant gene features in the expression profiles, and then, the minimum redundancy maximum relevance was applied to sort the remaining features. The generated feature-ranked list was fed into the incremental feature selection method to discover the essential genes and build powerful classifiers. The molecular mechanism of some biomarker genes was addressed using recent studies, and biological functions enriched by essential genes were examined. Our findings imply that genes including UBE2C, PCLAF, CDK1, CCNB1, MND1, APOBEC3G, TRAF3IP3, CD48, and GZMA play key roles in defining the different states and severity of COVID-19. Thus, a new point of reference is provided for understanding the disease's etiology and facilitating a precise therapy.
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Registered trials
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