ArticleStatistical methods in medical research2018
A big data pipeline: Identifying dynamic gene regulatory networks from time-course Gene Expression Omnibus data with applications to influenza infection.
Article in Statistical methods in medical research, 2018. 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.
- A novel group of genes that cause endocrine resistance in breast cancer identified by dynamic gene expression analysis.Oncotarget · 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
- Gene expression dynamic analysis reveals co-activation of Sonic Hedgehog and epidermal growth factor followed by dynamic silencing.Oncotarget · 2020Article
- A content-based dataset recommendation system for researchers-a case study on Gene Expression Omnibus (GEO) repository.Database : the journal of biological databases and curation · 2020Article
- Modelling of hypoxia gene expression for three different cancer cell lines.International journal of computational biology and drug design · 2020Article
- Identification of Monotonically Differentially Expressed Genes across Pathologic Stages for Cancers.Journal of oncology · 2020Article
- Investigation of temporal and spatial heterogeneities of the immune responses toInfectious Disease Modelling · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
A biological host response to an external stimulus or intervention such as a disease or infection is a dynamic process, which is regulated by an intricate network of many genes and their products. Understanding the dynamics of this gene regulatory network allows us to infer the mechanisms involved in a host response to an external stimulus, and hence aids the discovery of biomarkers of phenotype and biological function. In this article, we propose a modeling/analysis pipeline for dynamic gene expression data, called Pipeline4DGEData, which consists of a series of statistical modeling techniques to construct dynamic gene regulatory networks from the large volumes of high-dimensional time-course gene expression data that are freely available in the Gene Expression Omnibus repository. This pipeline has a consistent and scalable structure that allows it to simultaneously analyze a large number of time-course gene expression data sets, and then integrate the results across different studies. We apply the proposed pipeline to influenza infection data from nine studies and demonstrate that interesting biological findings can be discovered with its implementation.
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Registered trials
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