Abstract Motivation: High-density microarray technology permits the quantitative and simultaneous monitoring of thousands of genes. The interpretation challenge is to extract relevant information from this large amount of data. A growing variety of statistical analysis approaches are available to identify clusters of genes that share common expression characteristics, but provide no information regarding the biological similarities of genes within clusters. The published literature provides a potential source of information to assist in interpretation of clustering results. Results: We describe a data mining method that uses indexing terms (‘keywords’) from the published literature linked to specific genes to present a view of the conceptual similarity of genes within a cluster or group of interest. The method takes advantage of the hierarchical nature of Medical Subject Headings used to index citations in the MEDLINE database, and the registry numbers applied to enzymes. Availability: We have created a publicly accessible website that provides this form of gene expression interpretation at http://www.array.ucsd.edu. Contact: [email protected] 8 To whom correspondence should be addressed at: University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093-0602, USA. 7 Present address: Genomics Institute of the Novartis Research Foundation, 3115 Merryfield Row, San Diego, CA 92121, USA.