ArticleFrontiers in immunology2022
Exploration of blood-derived coding and non-coding RNA diagnostic immunological panels for COVID-19 through a co-expressed-based machine learning procedure.
Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 20 citations in OpenAlex.
- Comparative review of artificial intelligence for transcriptomic biomarker discovery in coronavirus disease 2019 (COVID-19).Briefings in bioinformatics · 2026Review
- Bioinformatics Analysis of Persistent Dysregulated Expression of Genes Involved in HCV-Induced Neurological Disorders and Liver Injuries After DAA Treatment Through Weighted Gene Co-Expression Network Analysis.Health science reports · 2025Article
- Exploring genetic signatures of obesity: hub genes and miRNAs unveiled through comprehensive bioinformatic analysis.Journal of diabetes and metabolic disorders · 2024Article
- Revealing disease subtypes and heterogeneity in common variable immunodeficiency through transcriptomic analysis.Scientific reports · 2024Article
- SARS-CoV-2 remodels the landscape of small non-coding RNAs with infection time and symptom severity.NPJ systems biology and applications · 2024Article
- Neurological complications after COVID-19: A narrative review.eNeurologicalSci · 2023Review
- Effect of the SARS-CoV-2 Delta-associated G15U mutation on the s2m element dimerization and its interactions with miR-1307-3p.RNA (New York, N.Y.) · 2023Article
- Gene biomarkers and classifiers for various subtypes of HTLV-1-caused ATLL cancer identified by a combination of differential gene co‑expression and support vector machine algorithms.Medical microbiology and immunology · 2023Article
- Potential miRNA-gene interactions determining progression of various ATLL cancer subtypes after infection by HTLV-1 oncovirus.BMC medical genomics · 2023Article
- Classification of COVID-19 Patients into Clinically Relevant Subsets by a Novel Machine Learning Pipeline Using Transcriptomic Features.International journal of molecular sciences · 2023Article
- Identification of novel miRNAs potentially involved in the pathogenesis of adult T-cell leukemia/lymphoma using WGCNA followed by RT-qPCR test of hub genes.Infectious agents and cancer · 2023Article
- Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations.PloS one · 2023Article
- Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review.Diabetology & metabolic syndrome · 2022Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS- CoV-2) is the causative virus of the pandemic coronavirus disease 2019 (COVID-19). Evaluating the immunological factors and other implicated processes underlying the progression of COVID-19 is essential for the recognition and then the design of efficacious therapies. Therefore, we analyzed RNAseq data obtained from PBMCs of the COVID-19 patients to explore coding and non-coding RNA diagnostic immunological panels. For this purpose, we integrated multiple RNAseq data and analyzed them overall as well as by considering the state of disease including severe and non-severe conditions. Afterward, we utilized a co-expressed-based machine learning procedure comprising weighted-gene co-expression analysis and differential expression gene as filter phase and recursive feature elimination-support vector machine as wrapper phase. This procedure led to the identification of two modules containing 5 and 84 genes which are mostly involved in cell dysregulation and innate immune suppression, respectively. Moreover, the role of vitamin D in regulating some classifiers was highlighted. Further analysis disclosed the role of discriminant miRNAs including miR-197-3p, miR-150-5p, miR-340-5p, miR-122-5p, miR-1307-3p, miR-34a-5p, miR-98-5p and their target genes comprising
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