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Open Access Highly Accessed Open Badges Research article

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

Julie L Morrison12*, Rainer Breitling13, Desmond J Higham2 and David R Gilbert1

Author Affiliations

1 Bioinformatics Research Centre, University of Glasgow, Glasgow, UK

2 Department of Mathematics, University of Strathclyde, Glasgow, UK

3 Institute of Biomedical and Life Sciences, Glasgow, UK

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BMC Bioinformatics 2005, 6:233  doi:10.1186/1471-2105-6-233

Published: 21 September 2005



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.


GeneRank is an intuitive modification of PageRank that maintains many of its mathematical properties. It combines gene expression information with a network structure derived from gene annotations (gene ontologies) or expression profile correlations. Using both simulated and real data we find that the algorithm offers an improved ranking of genes compared to pure expression change rankings.


Our modification of the PageRank algorithm provides an alternative method of evaluating microarray experimental results which combines prior knowledge about the underlying network. GeneRank offers an improvement compared to assessing the importance of a gene based on its experimentally observed fold-change alone and may be used as a basis for further analytical developments.