BACKGROUND: In the post-genomic era, the development of high-throughput gene 
expression detection technology provides huge amounts of experimental data, 
which challenges the traditional pipelines for data processing and analyzing in 
scientific researches.
RESULTS: In our work, we integrated gene expression information from Gene 
Expression Omnibus (GEO), biomedical ontology from Medical Subject Headings 
(MeSH) and signaling pathway knowledge from sigPathway entries to develop a 
context mining tool for gene expression analysis - GEOGLE. GEOGLE offers a rapid 
and convenient way for searching relevant experimental datasets, pathways and 
biological terms according to multiple types of queries: including biomedical 
vocabularies, GDS IDs, gene IDs, pathway names and signature list. Moreover, 
GEOGLE summarizes the signature genes from a subset of GDSes and estimates the 
correlation between gene expression and the phenotypic distinction with an 
integrated p value.
CONCLUSION: This approach performing global searching of expression data may 
expand the traditional way of collecting heterogeneous gene expression 
experiment data. GEOGLE is a novel tool that provides researchers a quantitative 
way to understand the correlation between gene expression and phenotypic 
distinction through meta-analysis of gene expression datasets from different 
experiments, as well as the biological meaning behind. The web site and user 
guide of GEOGLE are available at: 
http://omics.biosino.org:14000/kweb/workflow.jsp?id=00020.