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This article is part of the supplement: Proceedings of the Third Annual RECOMB Satellite Workshop on Massively Parallel Sequencing (RECOMB-seq 2013)

Open Access Proceedings

Prioritization of candidate disease genes by topological similarity between disease and protein diffusion profiles

Jie Zhu1, Yufang Qin2, Taigang Liu2, Jun Wang13 and Xiaoqi Zheng13*

Author Affiliations

1 Department of Mathematics, Shanghai Normal University, Shanghai 200034, China

2 College of Information Technology, Shanghai Ocean University, Shanghai 201306, China

3 Scientific Computing Key Laboratory of Shanghai Universities, Shanghai 200234, China

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BMC Bioinformatics 2013, 14(Suppl 5):S5  doi:10.1186/1471-2105-14-S5-S5

Published: 10 April 2013

Abstract

Background

Identification of gene-phenotype relationships is a fundamental challenge in human health clinic. Based on the observation that genes causing the same or similar phenotypes tend to correlate with each other in the protein-protein interaction network, a lot of network-based approaches were proposed based on different underlying models. A recent comparative study showed that diffusion-based methods achieve the state-of-the-art predictive performance.

Results

In this paper, a new diffusion-based method was proposed to prioritize candidate disease genes. Diffusion profile of a disease was defined as the stationary distribution of candidate genes given a random walk with restart where similarities between phenotypes are incorporated. Then, candidate disease genes are prioritized by comparing their diffusion profiles with that of the disease. Finally, the effectiveness of our method was demonstrated through the leave-one-out cross-validation against control genes from artificial linkage intervals and randomly chosen genes. Comparative study showed that our method achieves improved performance compared to some classical diffusion-based methods. To further illustrate our method, we used our algorithm to predict new causing genes of 16 multifactorial diseases including Prostate cancer and Alzheimer's disease, and the top predictions were in good consistent with literature reports.

Conclusions

Our study indicates that integration of multiple information sources, especially the phenotype similarity profile data, and introduction of global similarity measure between disease and gene diffusion profiles are helpful for prioritizing candidate disease genes.

Availability

Programs and data are available upon request.