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Bioinformatics Advance Access published online on November 10, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl569
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received July 7, 2006
Revised October 23, 2006
Accepted November 3, 2006

Article

CGI: a new approach for prioritizing genes by Combining Gene expression and protein-protein Interaction data

Xiaotu Ma 1 c, Hyunju Lee 2 c, Li Wang 1, and Fengzhu Sun 1 *

1 Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089-2910, USA
2 Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089-2910, USA; Department of Computer Science, University of Southern California, Los Angeles, CA 90089-2910, USA; Present address: Harvard-Partners Center for Genetics and Genomics, 77 Avenue Louis Pasteur Boston, MA 02115

* To whom correspondence should be addressed.
Fengzhu Sun, E-mail: fsun{at}usc.edu


  Abstract

Motivation: Identifying candidate genes associated with a given phenotype or trait is an important problem in biological and biomedical studies. Prioritizing genes based on the accumulated information from several data sources is of fundamental importance. Several integrative methods have been developed when a set of candidate genes for the phenotype is available. However, how to prioritize genes for phenotypes when no candidates are available is still a challenging problem.

Results: We develop a new method for prioritizing genes associated with a phenotype by Combining Gene expression and protein Interaction data (CGI). The method is applied to yeast gene expression data sets in combination with protein interaction data sets of varying reliability. We found that our method outperforms the intuitive prioritizing method of using either gene expression data or protein interaction data only and a recent gene ranking algorithm GeneRank. We then apply our method to prioritize genes for Alzheimer's disease.

Availability: The code in this paper is available upon request.

Associate Editor: Trey Ideker

c These two authors contribute equally.


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