ReviewComputers in biology and medicine2022
Network for network concept offers new insights into host- SARS-CoV-2 protein interactions and potential novel targets for developing antiviral drugs.
Review in Computers in biology and medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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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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
10 citing papers in PubMed, 23 citations in OpenAlex.
- HuR enhances SARS-CoV-2 non-structural protein translation through the genomic 5'-UTR, by promoting polypyrimidine tract-binding protein binding.Journal of virology · 2026Article
- When Viruses Talk through Extracellular Vesicles: a New Perspective on Sars-Cov-2-Induced Neurodegeneration.Journal of extracellular vesicles · 2026Article
- Integrated gene network analysis and experimental validation identify key hub genes in potato response to Potato Virus Y infection.PloS one · 2025Article
- Priority index for critical Covid-19 identifies clinically actionable targets and drugs.Communications biology · 2024Article
- In silico drug repurposing carvedilol and its metabolites against SARS-CoV-2 infection using molecular docking and molecular dynamic simulation approaches.Scientific reports · 2023Article
- Strategies and Trends in COVID-19 Vaccination Delivery: What We Learn and What We May Use for the Future.Vaccines · 2023Article
- Ghosts of the past: Elemental composition, biosynthesis reactions and thermodynamic properties of Zeta P.2, Eta B.1.525, Theta P.3, Kappa B.1.617.1, Iota B.1.526, Lambda C.37 and Mu B.1.621 variants of SARS-CoV-2.Microbial risk analysis · 2023Article
- Analysis of age-dependent gene-expression in human tissues for studying diabetes comorbidities.Scientific reports · 2023Article
- Systematic Guidelines for Effective Utilization of COVID-19 Databases in Genomic, Epidemiologic, and Clinical Research.Viruses · 2023Review
- Structural analysis of SARS-CoV-2 Spike protein variants through graph embedding.Network modeling and analysis in health informatics and bioinformatics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
SARS-CoV-2, the causal agent of COVID-19, is primarily a pulmonary virus that can directly or indirectly infect several organs. Despite many studies carried out during the current COVID-19 pandemic, some pathological features of SARS-CoV-2 have remained unclear. It has been recently attempted to address the current knowledge gaps on the viral pathogenicity and pathological mechanisms via cellular-level tropism of SARS-CoV-2 using human proteomics, visualization of virus-host protein-protein interactions (PPIs), and enrichment analysis of experimental results. The synergistic use of models and methods that rely on graph theory has enabled the visualization and analysis of the molecular context of virus/host PPIs. We review current knowledge on the SARS-COV-2/host interactome cascade involved in the viral pathogenicity through the graph theory concept and highlight the hub proteins in the intra-viral network that create a subnet with a small number of host central proteins, leading to cell disintegration and infectivity. Then we discuss the putative principle of the "gene-for-gene and "network for network" concepts as platforms for future directions toward designing efficient anti-viral therapies.
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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.