ArticleCommunications in statistics: theory and methods2009
Application of Biostatistics and Bioinformatics Tools to Identify Putative Transcription Factor-Gene Regulatory Network of Ankylosing Spondylitis and Sarcoidosis.
Article in Communications in statistics: theory and methods, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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.
The trial behind it
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
4 citing papers in PubMed.
- Molecular profiling in sarcoidosis.Current opinion in pulmonary medicine · 2020Review
- E2F transcription factors associated with up-regulated genes in glioblastoma.Cancer biomarkers : section A of Disease markers · 2017Article
- Hypothesis: sarcoidosis is a STAT1-mediated disease.Clinical immunology (Orlando, Fla.) · 2009Article
- Insights in to the pathogenesis of axial spondyloarthropathy based on gene expression profiles.Arthritis research & therapy · 2009Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Transcription factors and corresponding cis-regulatory elements are considered key components in gene regulation. We combined biostatistics and bioinformatics tools to streamline identification of putative transcription factor-gene regulatory networks unique for two immune-mediated diseases, ankylosing spondylitis and sarcoidosis. After identifying differentially expressed genes from microarrays, we employed tightCluster to find tight clusters of potentially co-regulated genes. By subsequently applying bioinformatics tools to search for common cis-regulatory elements, putative transcription factor-gene regulatory networks were found. Recognition of these networks by applying this methodology could pave the way for new insights into disease pathogenesis.
Identifiers
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Registered trials
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