Clustering microarray-derived gene lists through implicit literature relationships.
BACKGROUND
Microarrays rapidly generate large quantities of gene expression information, but interpreting such data within a biological context is still relatively complex and laborious. New methods that can identify functionally related genes via shared literature concepts will be useful in addressing these needs.


RESULTS
We have developed a novel method that uses implicit literature relationships (concepts related via shared, intermediate concepts) to cluster related genes. Genes are evaluated for implicit connections within a network of biomedical objects (other genes, ontological concepts and diseases) that are connected via their co-occurrences in Medline titles and/or abstracts. On the basis of these implicit relationships, individual gene pairs are scored using a probability-based algorithm. Scores are generated for all pairwise combinations of genes, which are then clustered based on the scores. We applied this method to a test set composed of nine functional groups with known relationships. The method scored highly for all nine groups and significantly better than a benchmark co-occurrence-based method for six groups. We then applied this method to gene sets specific to two previously defined breast tumor subtypes. Analysis of the results recapitulated known biological relationships and identified novel pathway relationships unique to each tumor subtype. We demonstrate that this method provides a valuable new means of identifying and visualizing significantly related genes within gene lists via their implicit relationships in the literature.
Burkart
Mark F
MF
Department of Internal Medicine, The McDermott Center for Human Growth and Development, Division of Translational Research, The University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. mark.burkart@utsouthwestern.edu
Wren
Jonathan D
JD
Herschkowitz
Jason I
JI
Perou
Charles M
CM
Garner
Harold R
HR
D000465:Algorithms; D016000:Cluster Analysis; D030562:Databases, Protein; D020869:Gene Expression Profiling / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D016239:MEDLINE; D009323:Natural Language Processing; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010506:Periodicals as Topic; D020543:Proteome / Q000145:classification* / Q000378:metabolism*
D020543:Proteome