Use of keyword hierarchies to interpret gene expression patterns

Motivation: High-density microarray technology permits the quantitative and simultaneous monitoring of thousands of genes. The interpretation challeng e is to extract relevant information from this large amount of data. A grow ing variety of statistical analysis approaches are available to identify cl usters of genes that share common expression characteristics, but provide n o information regarding the biological similarities of genes within cluster s. The published literature provides a potential source of information to a ssist in interpretation of clustering results. Results: We describe a data mining method that uses indexing terms ('keywor ds') from the published literature linked to specific genes to present a vi ew of the conceptual similarity of genes within a cluster or group of inter est. The method takes advantage of the hierarchical nature of Medical Subje ct Headings used to index citations in the MEDLINE database, and the regist ry numbers applied to enzymes.