ArticleBMC genomics2010
Identifying functional relationships within sets of co-expressed genes by combining upstream regulatory motif analysis and gene expression information.
Article in BMC genomics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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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Who cites it
3 citing papers in PubMed.
- Filtering of Data-Driven Gene Regulatory Networks UsingFrontiers in genetics · 2021Article
- Exploiting identifiability and intergene correlation for improved detection of differential expression.ISRN bioinformatics · 2013Article
- Nonlinear dependence in the discovery of differentially expressed genes.ISRN bioinformatics · 2012Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundExisting clustering approaches for microarray data do not adequately differentiate between subsets of co-expressed genes. We devised a novel approach that integrates expression and sequence data in order to generate functionally coherent and biologically meaningful subclusters of genes. Specifically, the approach clusters co-expressed genes on the basis of similar content and distributions of predicted statistically significant sequence motifs in their upstream regions.
resultsWe applied our method to several sets of co-expressed genes and were able to define subsets with enrichment in particular biological processes and specific upstream regulatory motifs.
conclusionsThese results show the potential of our technique for functional prediction and regulatory motif identification from microarray data.
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