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Node handprinting: a scalable and accurate algorithm for aligning multiple biological networks


Radu, A and Charleston, M, Node handprinting: a scalable and accurate algorithm for aligning multiple biological networks, Journal of Computational Biology, 22, (7) pp. 687-697. ISSN 1066-5277 (2015) [Refereed Article]

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Copyright Statement

Copyright 2015 Mary Ann Liebert, Inc.

DOI: doi:10.1089/cmb.2014.0247


Due to recent advancements in high-throughput sequencing technologies, progressively more protein–protein interactions have been identified for a growing number of species. Subsequently, the protein–protein interaction networks for these species have been further refined. The increase in the quality and availability of these networks has in turn brought a demand for efficient methods to analyze such networks. The pairwise alignment of these networks has been moderately investigated,with numerous algorithms available, but there is very little progress in the field of multiple network alignment. Multiple alignment of networks from different organisms is ideal at finding abnormally conserved or disparate subnetworks. We present a fast and accurate algorithmic approach, Node Handprinting (NH), based on our previous work with Node Fingerprinting, which enables quick and accurate alignment of multiple networks. We also propose two new metrics for the analysis of multiple alignments, as the current metrics are not as sophisticated as their pairwise alignment counterparts. To assess the performance of NH, we use previously aligned datasets as well as protein interaction networks generated from the public database BioGRID. Our results indicate that NH compares favorably with current methodologies and is the only algorithm capable of performing the more complex alignments.

Item Details

Item Type:Refereed Article
Keywords:multiple network alignment, network alignment, network comparison, Protein– protein interaction networks
Research Division:Biological Sciences
Research Group:Biochemistry and cell biology
Research Field:Systems biology
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the biological sciences
UTAS Author:Charleston, M (Professor Michael Charleston)
ID Code:100236
Year Published:2015
Web of Science® Times Cited:3
Deposited By:Mathematics and Physics
Deposited On:2015-05-07
Last Modified:2017-10-31
Downloads:262 View Download Statistics

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