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

BMC Bioinformatics

Proceedings

Yufang Qin¹  Taigang Liu¹  Jie Zhu²  Jun Wang³  Xiaoqi Zheng³ 

[1] College of Information Technology, Shanghai Ocean University, 201306, Shanghai, China;Department of Mathematics, Shanghai Normal University, 200034, Shanghai, China;Department of Mathematics, Shanghai Normal University, 200034, Shanghai, China;Scientific Computing Key Laboratory of Shanghai Universities, 200234, Shanghai, China;

关键词: Random Walk;    Diffusion Profile;    Random Walk With Restart;    Candidate Disease Gene;    Rank Ratio;   

DOI  :  10.1186/1471-2105-14-S5-S5

来源: Springer

【 摘 要 】

BackgroundIdentification 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.ResultsIn 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.ConclusionsOur 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.AvailabilityPrograms and data are available upon request.

【 授权许可】

Unknown   
© Zhu et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.