Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICDE.2007.367900
Title: Labeling network motifs in protein interactomes for protein function prediction
Authors: Chen, J.
Hsu, W. 
Lee, M.L. 
Ng, S.-K.
Issue Date: 2007
Source: Chen, J.,Hsu, W.,Lee, M.L.,Ng, S.-K. (2007). Labeling network motifs in protein interactomes for protein function prediction. Proceedings - International Conference on Data Engineering : 546-555. ScholarBank@NUS Repository. https://doi.org/10.1109/ICDE.2007.367900
Abstract: Biological networks such as the protein-protein interaction (PPI) network have been found to contain small recurring subnetworks in significantly higher frequencies than in random networks. Such network motifs are useful for uncovering structural design principles of complex biological networks. However, current network motif finding algorithms models the PPI network as a uni-labeled graph, discovering only unlabeled and thus relatively uninformative network motifs as a result. Our objective is to exploit the currently available biological information that are associated with the vertices (the proteins) to capture not only the topological shapes of the motifs, but also the biological context in which they occurred in the PPI networks for network motif applications. We present a method called LaMoFinder to label network motifs with Gene Ontology terms in a PPI network. We also show how the resulting labeled network motifs can be used to predict unknown protein functions. Experimental results showed that the labeled network motifs extracted are biologically meaningful and can achieve better performance than existing PPI topology based methods for predicting unknown protein functions. © 2007 IEEE.
Source Title: Proceedings - International Conference on Data Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/40777
ISBN: 1424408032
ISSN: 10844627
DOI: 10.1109/ICDE.2007.367900
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