Scalable multiple global network alignment for biological data

Advances in high-throughput technology has led to an increased amount of available data on protein-protein interaction (PPI) data. Detecting and extracting functional modules that are common across multiple networks is an important step towards understanding the role of functional modules and how they have evolved across species. A global protein-protein interaction network alignment algorithm attempts to find such functional orthologs across multiple networks. In this article, we propose a scalable global network alignment algorithm based on clustering methods and graph matching techniques in order to detect conserved interactions while simultaneously attempting to maximize the sequence similarity of nodes involved in the alignment. We present an algorithm for multiple alignments, in which several protein-protein interaction networks are aligned. We empirically evaluated our algorithm on several real biological datasets. We find that our approach offers a significant benefit both in terms of quality as well as speed over the state-of-the-art.

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