Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

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

For all author emails, please log on.

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

Published: 21 September 2005

Additional files

Additional File 1:

The Matlab GeneRank implementation. The file contains a Matlab implementation of the GeneRank algorithm. The file requires a matrix describing the network connectivity and a vector of expression changes for each gene. The output is the vector of rankings for each gene.

Format: M Size: 1KB Download file

Open Data

Additional File 2:

A Matlab .mat file containing sample GO networks and expression change vectors. This file can be loaded into Matlab using the command load G0_matrix. This will load three matrices (w_All, w_Up and w_Down) and three expression change vectors (expr_data, expr_dataUp and expr_dataDown) into the current workspace. These matrices were constructed using the all three sections of the Gene Ontology, where a link is present between two genes if they share a GO annotation. Only genes which are up-regulated are included in w_Up and only down-regulated in w_Down. The GeneRank algorithm should be used with the corresponding matrix and expression change vector, e.g. the command ranking = geneRank(w_Up, expr_dataUp,d) would be used to calculate the ranking of the up-regulated genes in the experiment.

Format: MAT Size: 6.2MB Download file

Open Data