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Hierarchical closeness efficiently predicts disease genes in a directed signaling network

Paper ID Volume ID Publish Year Pages File Format Full-Text
15083 1374 2014 7 PDF Available
Title
Hierarchical closeness efficiently predicts disease genes in a directed signaling network
Abstract

•Devised a novel structural measure called hierarchical closeness (HC) to rank disease risk of genes in a directed biological network.•Hierarchical closeness outperforms the other well-known structural centrality measures, particularly for cancer, hereditary, immune, and neurodegenerative disease-related genes in a human signaling network.•The set of HC-center genes are different from the hub genes with the largest interactions.•Genes with relatively high hierarchical closeness are significantly likely to encode proteins in the extracellular matrix and receptor proteins in a human signaling network.•All the findings were reproduced in a random Boolean network model.

BackgroundMany structural centrality measures were proposed to predict putative disease genes on biological networks. Closeness is one of the best-known structural centrality measures, and its effectiveness for disease gene prediction on undirected biological networks has been frequently reported. However, it is not clear whether closeness is effective for disease gene prediction on directed biological networks such as signaling networks.ResultsIn this paper, we first show that closeness does not significantly outperform other well-known centrality measures such as Degree, Betweenness, and PageRank for disease gene prediction on a human signaling network. In addition, we observed that prediction accuracy by the closeness measure was worse than that by a reachability measure, but closeness could efficiently predict disease genes among a set of genes with the same reachability value. Based on this observation, we devised a novel structural measure, hierarchical closeness, by combining reachability and closeness such that all genes are first ranked by the degree of reachability and then the tied genes are further ranked by closeness. We discovered that hierarchical closeness outperforms other structural centrality measures in disease gene prediction. We also found that the set of highly ranked genes in terms of hierarchical closeness is clearly different from that of hub genes with high connectivity. More interestingly, these findings were consistently reproduced in a random Boolean network model. Finally, we found that genes with relatively high hierarchical closeness are significantly likely to encode proteins in the extracellular matrix and receptor proteins in a human signaling network, supporting the fact that half of all modern medicinal drugs target receptor-encoding genes.ConclusionTaken together, hierarchical closeness proposed in this study is a novel structural measure to efficiently predict putative disease genes in a directed signaling network.

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Keywords
Hierarchical closeness; Disease gene prediction; Signaling network; Boolean network
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Hierarchical closeness efficiently predicts disease genes in a directed signaling network
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 53, Part B, December 2014, Pages 191–197
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us