Predicting disease-related genes by topological similarity in human protein-protein interaction network

• Authors: Lei Zhang , Ke Hu , Yi Tang
• Languages of publication: EN

Abstracts

EN

Predicting genes likely to be involved in human diseases is an important task in bioinformatics field. Nowadays, the accumulation of human protein-protein interactions (PPIs) data provides us an unprecedented opportunity to gain insight into human diseases. In this paper, we adopt the topological similarity in human protein-protein interaction network to predict disease-related genes. As a computational algorithm to speed up the identification of disease-related genes, the topological similarity has substantial advantages over previous topology-based algorithms. First of all, it provides a global measurement of similarity between two vertices. Secondly, quantity which can measure new topological feature has been integrated into the notion of topological similarity. Our method is specially designed for predicting disease-related genes of single disease-gene family. The proposed method is applied to human protein-protein interaction and hepatocellular carcinoma (HCC) data. The results show a significant enrichment of disease-related genes that are characterized by higher topological similarity than other genes.

Keywords

EN

predicting disease genes; protein-protein interactions; topological features; topological similarity

Discipline

• 87.10.Vg: Biological information
• 87.15.km: Protein-protein interactions

• Publisher: De Gruyter Open
• Journal: Open Physics
• Year: 2010
• Volume: 8
• Issue: 4
• Pages: 672-682

Dates

published

1 - 8 - 2010

online

22 - 5 - 2010

Contributors

author

Lei Zhang

• Department of physics, University of Xiangtan, Xiangtan, 411105, Hunan, China

author

Ke Hu

• Department of physics, University of Xiangtan, Xiangtan, 411105, Hunan, China

author

Yi Tang

• Department of physics, University of Xiangtan, Xiangtan, 411105, Hunan, China

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Identifiers

DOI

10.2478/s11534-009-0114-9