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 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.

[1]  M M Waldrop.  Learning to drink from a fire hose. , 1990, Science.

[2]  D. Eisenberg,et al.  A combined algorithm for genome-wide prediction of protein function , 1999, Nature.

[3]  J. Hudson,et al.  Development of a prostate cDNA microarray and statistical gene expression analysis package , 2000, Molecular carcinogenesis.

[4]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[5]  Hagit Shatkay,et al.  Genes, Themes, and Microarrays: Using Information Retrieval for Large-Scale Gene Analysis , 2000, ISMB.

[6]  L Hunter,et al.  MedMiner: an Internet text-mining tool for biomedical information, with application to gene expression profiling. , 1999, BioTechniques.

[7]  Christian A. Rees,et al.  Systematic variation in gene expression patterns in human cancer cell lines , 2000, Nature Genetics.

[8]  J. Mesirov,et al.  Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[9]  G. Church,et al.  Systematic determination of genetic network architecture , 1999, Nature Genetics.

[10]  Ronald W. Davis,et al.  A genome-wide transcriptional analysis of the mitotic cell cycle. , 1998, Molecular cell.

[11]  M. Bittner,et al.  Data management and analysis for gene expression arrays , 1998, Nature Genetics.

[12]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.