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Node fingerprinting: an efficient heuristic for aligning biological networks


Radu, A and Charleston, M, Node fingerprinting: an efficient heuristic for aligning biological networks, Journal of Computational Biology, 21, (10) pp. 760-770. ISSN 1066-5277 (2014) [Refereed Article]

Copyright Statement

Copyright 2014 Mary Ann Liebert, Inc.

DOI: doi:10.1089/cmb.2014.0114


With the continuing increase in availability of biological data and improvements to biological models, biological network analysis has become a promising area of research. An emerging technique for the analysis of biological networks is through network alignment. Network alignment has been used to calculate genetic distance, similarities between regulatory structures, and the effect of external forces on gene expression, and to depict conditional activity of expression modules in cancer. Network alignment is algorithmically complex, and therefore we must rely on heuristics, ideally as efficient and accurate as possible. The majority of current techniques for network alignment rely on precomputed information, such as with protein sequence alignment, or on tunable network alignment parameters, which may introduce an increased computational overhead. Our presented algorithm, which we call Node Fingerprinting (NF), is appropriate for performing global pairwise network alignment without precomputation or tuning, can be fully parallelized, and is able to quickly compute an accurate alignment between two biological networks. It has performed as well as or better than existing algorithms on biological and simulated data, and with fewer computational resources. The algorithmic validation performed demonstrates the low computational resource requirements of NF.

Item Details

Item Type:Refereed Article
Keywords:gene networks, 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:100235
Year Published:2014
Web of Science® Times Cited:5
Deposited By:Mathematics and Physics
Deposited On:2015-05-07
Last Modified:2017-10-31

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