title: Gene set enrichment; a problem of pathways.
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.
authors.affiliation: Institute of Psychiatry, Kings College London, UK. matthew.1.davies@kcl.ac.uk
authors.forename: Matthew N
authors.initials: MN
authors.lastname: Davies
authors.forename: Emma L
authors.initials: EL
authors.lastname: Meaburn
authors.forename: Leonard C
authors.initials: LC
authors.lastname: Schalkwyk
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D015870:Gene Expression; D055106:Genome-Wide Association Study; D006801:Humans; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D017423:Sequence Analysis, RNA