Discovering disease-associated genes in weighted protein–protein interaction networks

Ying Cui, Meng Cai and H. Eugene Stanley

Physica A: Statistical Mechanics and its Applications, 2018, vol. 496, issue C, 53-61

Abstract: Although there have been many network-based attempts to discover disease-associated genes, most of them have not taken edge weight – which quantifies their relative strength – into consideration. We use connection weights in a protein–protein interaction (PPI) network to locate disease-related genes. We analyze the topological properties of both weighted and unweighted PPI networks and design an improved random forest classifier to distinguish disease genes from non-disease genes. We use a cross-validation test to confirm that weighted networks are better able to discover disease-associated genes than unweighted networks, which indicates that including link weight in the analysis of network properties provides a better model of complex genotype–phenotype associations.

Keywords: Disease gene discovering; Topological properties; Weighted PPI network; Machine learning (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:496:y:2018:i:c:p:53-61

DOI: 10.1016/j.physa.2017.12.080

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