ArticleBioMed research international2014
Identification of Influenza A/H7N9 virus infection-related human genes based on shortest paths in a virus-human protein interaction network.
Article in BioMed research international, 2014. 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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Who cites it
7 citing papers in PubMed, 14 citations in OpenAlex.
- Computational Identification of Guillain-Barré Syndrome-Related Genes by an mRNA Gene Expression Profile and a Protein-Protein Interaction Network.Frontiers in molecular neuroscience · 2022Article
- Differential proteomic analysis of children infected with respiratory syncytial virus.Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologica · 2021Article
- Identification of COVID-19 Infection-Related Human Genes Based on a Random Walk Model in a Virus-Human Protein Interaction Network.BioMed research international · 2020Article
- Identification of Triple-Negative Breast Cancer Genes and a Novel High-Risk Breast Cancer Prediction Model Development Based on PPI Data and Support Vector Machines.Frontiers in genetics · 2019Article
- Global temporal dynamic landscape of pathogen-mediated subversion of Arabidopsis innate immunity.Scientific reports · 2017Article
- Article
- Computational identification of surrogate genes for prostate cancer phases using machine learning and molecular network analysis.Theoretical biology & medical modelling · 2014Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recently emerging Influenza A/H7N9 virus is reported to be able to infect humans and cause mortality. However, viral and host factors associated with the infection are poorly understood. It is suggested by the "guilt by association" rule that interacting proteins share the same or similar functions and hence may be involved in the same pathway. In this study, we developed a computational method to identify Influenza A/H7N9 virus infection-related human genes based on this rule from the shortest paths in a virus-human protein interaction network. Finally, we screened out the most significant 20 human genes, which could be the potential infection related genes, providing guidelines for further experimental validation. Analysis of the 20 genes showed that they were enriched in protein binding, saccharide or polysaccharide metabolism related pathways and oxidative phosphorylation pathways. We also compared the results with those from human rhinovirus (HRV) and respiratory syncytial virus (RSV) by the same method. It was indicated that saccharide or polysaccharide metabolism related pathways might be especially associated with the H7N9 infection. These results could shed some light on the understanding of the virus infection mechanism, providing basis for future experimental biology studies and for the development of effective strategies for H7N9 clinical therapies.
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