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A novel predictor for disease-genes based on combination use of topological features in human protein-protein interaction network

Xi-qiang Zhao, Hong-hong Feng


Generally, complex human diseases are caused by multiple gene mutations, and these mutated genes affect the development of disease through interactions. It is believed that the structure of a network indicates its function, and the stability of the network is determined by some crucial points. In this paper, the centricity of the proteins (genes) in human protein-protein interaction network is considered as an indicator to distinguish disease and non-disease proteins. These features include K, BC, and Disease_C. And we find that disease-genes are more topological important than non-disease genes. Further, SVM classifier base on our topological features is created and gains a good prediction accuracy of 69.33% in 10-fold cross-validation test. The experimental results reveal that these three features can improve the prediction of disease-genes, and it demonstrates that the centricity of the proteins (genes) is helpful for finding key proteins in the PPI network.


crucial point, centricity, protein-protein interaction, topological feature, disease-genes

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