ArticleJournal of pharmacokinetics and pharmacodynamics2014
Dynamic transcriptional signatures and network responses for clinical symptoms in influenza-infected human subjects using systems biology approaches.
Article in Journal of pharmacokinetics and pharmacodynamics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- New developments in the biology of fibroblast growth factors.WIREs mechanisms of disease · 2022Review
- Fibroblast growth factor-9 expression in airway epithelial cells amplifies the type I interferon response and alters influenza A virus pathogenesis.PLoS pathogens · 2022Article
- An informatics research platform to make public gene expression time-course datasets reusable for more scientific discoveries.Database : the journal of biological databases and curation · 2020Article
- Investigation of temporal and spatial heterogeneities of the immune responses toInfectious Disease Modelling · 2019Article
- Restructured GEO: restructuring Gene Expression Omnibus metadata for genome dynamics analysis.Database : the journal of biological databases and curation · 2019Article
- The Antibody-Secreting Cell Response to Infection: Kinetics and Clinical Applications.Frontiers in immunology · 2017Review
- Controllability and stability analysis of large transcriptomic dynamic systems for host response to influenza infection in human.Infectious Disease Modelling · 2016Article
- Correlation-based iterative clustering methods for time course data: The identification of temporal gene response modules for influenza infection in humans.Infectious Disease Modelling · 2016Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Recent studies demonstrate that human blood transcriptional signatures may be used to support diagnosis and clinical decisions for acute respiratory viral infections such as influenza. In this article, we propose to use a newly developed systems biology approach for time course gene expression data to identify significant dynamically response genes and dynamic gene network responses to viral infection. We illustrate the methodological pipeline by reanalyzing the time course gene expression data from a study with healthy human subjects challenged by live influenza virus. We observed clear differences in the number of significant dynamic response genes (DRGs) between the symptomatic and asymptomatic subjects and also identified DRG signatures for symptomatic subjects with influenza infection. The 505 common DRGs shared by the symptomatic subjects have high consistency with the signature genes for predicting viral infection identified in previous works. The temporal response patterns and network response features were carefully analyzed and investigated.
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Registered trials
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