ArticleIET systems biology2023
Bioinformatics approach to identify the hub gene associated with COVID-19 and idiopathic pulmonary fibrosis.
Article in IET systems biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed, 8 citations in OpenAlex.
- Deciphering the functions of NELL2 in tumorigenesis and beyond.Frontiers in immunology · 2026Review
- Transcriptomic and miRNA Signatures of ChAdOx1 nCoV-19 Vaccine Response Using Machine Learning.Life (Basel, Switzerland) · 2025Article
- Diagnostic biomarkers and immune infiltration profiles common to COVID-19, acute myocardial infarction and acute ischaemic stroke using bioinformatics methods and machine learning.BMC neurology · 2025Article
- Blood transcriptomics analysis offers insights into variant-specific immune response to SARS-CoV-2.Scientific reports · 2024Article
- Whole blood transcriptomics reveals the enrichment of neutrophil activation pathways during erythema nodosum leprosum reaction.Frontiers in immunology · 2024Article
- Bioinformatics approach to identify the hub gene associated with COVID-19 and idiopathic pulmonary fibrosis.IET systems biology · 2023Article
- Blood transcriptomics analysis offers insights into variant-specific immune response to SARS-CoV-2.bioRxiv : the preprint server for biology · 2023Article
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
Abstract
The coronavirus disease 2019 (COVID-19) has developed into a global health crisis. Pulmonary fibrosis, as one of the complications of SARS-CoV-2 infection, deserves attention. As COVID-19 is a new clinical entity that is constantly evolving, and many aspects of disease are remain unknown. The datasets of COVID-19 and idiopathic pulmonary fibrosis were obtained from the Gene Expression Omnibus. The hub genes were screened out using the Random Forest (RF) algorithm depending on the severity of patients with COVID-19. A risk prediction model was developed to assess the prognosis of patients infected with SARS-CoV-2, which was evaluated by another dataset. Six genes (named NELL2, GPR183, S100A8, ALPL, CD177, and IL1R2) may be associated with the development of PF in patients with severe SARS-CoV-2 infection. S100A8 is thought to be an important target gene that is closely associated with COVID-19 and pulmonary fibrosis. Construction of a neural network model was successfully predicted the prognosis of patients with COVID-19. With the increasing availability of COVID-19 datasets, bioinformatic methods can provide possible predictive targets for the diagnosis, treatment, and prognosis of the disease and show intervention directions for the development of clinical drugs and vaccines.
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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 Socratic graph.