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Computational Methods for Protein–Protein Interaction Network Alignment

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Recent Advances in Biological Network Analysis
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Abstract

Advanced experimental and computational techniques have produced a large amount of biological data. Efficient analysis and mining of this complex data is critical to the understanding of biomechanism and evolution. Protein–protein interaction (PPI) data is important for understanding biological processes at the system level. Comparative analysis of PPI networks of various species may yield valuable information, such as conserved subnetwork motifs and pathways, and help with protein function prediction. Nevertheless, computationally PPI network alignment is a challenging problem and quite a few methods have been developed to address it.

This chapter presents three network alignment methods developed in the past few years based upon different principles. They combine sequence information, network topology, and subnetwork module information to score a PPI network alignment, and then employ two efficient algorithms (heuristics and convex optimization) to build alignments by optimizing the alignment scores. Experimental results show that these methods may build pairwise or multiple PPI network alignments efficiently, with accuracy favorably comparable to other methods.

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Ge, R., Wu, Q., Xu, J. (2021). Computational Methods for Protein–Protein Interaction Network Alignment. In: Yoon, BJ., Qian, X. (eds) Recent Advances in Biological Network Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-57173-3_3

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