GeneRank: using search engine technology for the analysis of microarray experiments

Interpretation of simple microarray experiments is usually based on the fold-change of gene expression between a reference and a 'treated' sample where the treatment can be of many types from drug exposure to genetic variation. Interpretation of the results usually combines lists of differentially expressed genes with previous knowledge about their biological function. Here we evaluate a method - based on the PageRank algorithm employed by the popular search engine Google - that tries to automate some of this procedure to generate prioritized gene lists by exploiting biological background information.