A hybrid network-based method for the detection of disease-related genes

Ying Cui, Meng Cai, Yang Dai and H. Eugene Stanley

Physica A: Statistical Mechanics and its Applications, 2018, vol. 492, issue C, 389-394

Abstract: Detecting disease-related genes is crucial in disease diagnosis and drug design. The accepted view is that neighbors of a disease-causing gene in a molecular network tend to cause the same or similar diseases, and network-based methods have been recently developed to identify novel hereditary disease-genes in available biomedical networks. Despite the steady increase in the discovery of disease-associated genes, there is still a large fraction of disease genes that remains under the tip of the iceberg. In this paper we exploit the topological properties of the protein–protein interaction (PPI) network to detect disease-related genes. We compute, analyze, and compare the topological properties of disease genes with non-disease genes in PPI networks. We also design an improved random forest classifier based on these network topological features, and a cross-validation test confirms that our method performs better than previous similar studies.

Keywords: Disease gene detection; Topological properties; PPI network; Random forest (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:492:y:2018:i:c:p:389-394

DOI: 10.1016/j.physa.2017.10.026

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