CGI: a new approach for prioritizing genes by combining gene expression and protein--protein interaction data

Authors:
Xiaotu Ma;Hyunju Lee;Li Wang;Fengzhu Sun
Affiliations:
Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California Los Angeles, CA 90089-2910, USA;Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California Los Angeles, CA 90089-2910, USA;Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California Los Angeles, CA 90089-2910, USA;Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California Los Angeles, CA 90089-2910, USA
Venue:
Bioinformatics
Year:
2007

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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. Contact: fsun@usc.edu Supplementary data: Supplementary data are available at Bioinformatics online.