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Graphlet alignment in protein interaction networks

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2 Author(s)
Mu-Fen Hsieh ; Department of Computer Science and Engineering, Texas A&M University, College Station, 77843, USA ; Sing-Hoi Sze

With the increased availability of genome-scale data, it becomes possible to study functional relationships of genes across multiple biological networks. While most previous approaches for studying conservation of patterns in networks are through the application of network alignment algorithms or the identification of network motifs, we show that it is possible to exhaustively enumerate all graphlet alignments, which consist of subgraphs from each network that share a common topology and contain homologous proteins at the same position in the topology. We show that our algorithm is able to cover significantly more proteins than previous network alignment algorithms while achieving comparable specificity and higher sensitivity with respect to functional enrichment.

Published in:

Genomic Signal Processing and Statistics (GENSIPS), 2010 IEEE International Workshop on

Date of Conference:

10-12 Nov. 2010