Sep 24, 2010

Gene set enrichment; a problem of pathways

Briefings in Functional Genomics
Matthew N DaviesLeonard C Schalkwyk

Abstract

Gene Set Enrichment (GSE) is a computational technique which determines whether a priori defined set of genes show statistically significant differential expression between two phenotypes. Currently, the gene sets used for GSE are derived from annotation or pathway databases, which often contain computationally based and unrepresentative data. Here, we propose a novel approach for the generation of comprehensive and biologically derived gene sets, deriving sets through the application of machine learning techniques to gene expression data. These gene sets can be produced for specific tissues, developmental stages or environments. They provide a powerful and functionally meaningful way in which to mine genomewide association and next generation sequencing data in order to identify disease-associated variants and pathways.

  • References29
  • Citations2

Mentioned in this Paper

Genome-Wide Association Study
Biochemical Pathway
Sequence Determinations, RNA
Gene Expression
Knowledge Representation (Computer)
Massively-Parallel Sequencing
Single Nucleotide Polymorphism
Computational Molecular Biology

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