MPGM: Scalable and Accurate Multiple Network Alignment.
IEEE/ACM transactions on computational biology and bioinformatics(2018)
摘要
Protein-protein interaction (PPI) network alignment is a canonical operation to transfer biological knowledge among species. The alignment of PPI-networks has many applications, such as the prediction of protein function and detection of conserved network motifs. A good multiple-network alignment (MNA) provides a deep understanding of biological networks and system-level cellular processes. With the massive amounts of available PPI data and the increasing number of known PPI networks, the problem of MNA is gaining more attention. In this paper, we introduce a new scalable and accurate algorithm, called MPGM, for aligning multiple networks. MPGM has two main steps: SEEDGENERATION and MULTIPLEPERCOLATION. In the first step, to generate an initial set of seed tuples, SEEDGENERATION uses only protein sequence similarities. In the second step, to align remaining unmatched nodes, MULTIPLEPERCOLATION algorithm uses network structures and the generated seed tuples. We show that, with respect to different evaluation criteria, MPGM outperforms the other state-of-the-art algorithms. In addition, we guarantee the performance of MPGM under certain classes of network models. We introduce a stochastic model for generating k correlated networks. We prove that for this model MULTIPLEPERCOLATION correctly aligns almost all the nodes. Our theoretical results are supported by experimental evaluations over synthetic networks.
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关键词
Multiple network alignment, protein-protein interaction, percolation graph matching, biological networks
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