Predicting disease-related genes by path-based similarity and community structure in protein-protein interaction network

TL;DR

This paper introduces a novel network-based method combining path-based similarity and community structure analysis in protein-protein interaction networks to improve disease-gene prediction accuracy.

Contribution

It proposes new similarity indices and demonstrates the importance of community structure in enhancing disease-gene prediction performance.

Findings

Genes related to similar diseases tend to be in the same community.

Community structure significantly improves prediction accuracy.

The combined similarity measure outperforms traditional methods.

Abstract

Network-based computational approaches to predict unknown genes associated with certain diseases are of considerable significance for uncovering the molecular basis of human diseases. In this paper, we proposed a kind of new disease-gene-prediction methods by combining the path-based similarity with the community structure in the human protein-protein interaction network. Firstly, we introduced a set of path-based similarity indices, a novel community-based similarity index, and a new similarity combining the path-based similarity index. Then we assessed the statistical significance of the measures in distinguishing the disease genes from non-disease genes, to confirm their availability in predicting disease genes. Finally, we applied these measures to the disease-gene prediction of single disease-gene family, and analyzed the performance of these measures in disease-gene prediction, especially the effect of the community structure on the prediction performance in detail. The results indicated that genes associated with the same or similar diseases commonly reside in the same community of the protein-protein interaction network, and the community structure is greatly helpful for the disease-gene prediction.