title: Accurate multiple network alignment through context-sensitive random walk.
abstract: BACKGROUND
  Comparative network analysis can provide an effective means of analyzing large-scale biological networks and gaining novel insights into their structure and organization. Global network alignment aims to predict the best overall mapping between a given set of biological networks, thereby identifying important similarities as well as differences among the networks. It has been shown that network alignment methods can be used to detect pathways or network modules that are conserved across different networks. Until now, a number of network alignment algorithms have been proposed based on different formulations and approaches, many of them focusing on pairwise alignment.
  
  
  RESULTS
  In this work, we propose a novel multiple network alignment algorithm based on a context-sensitive random walk model. The random walker employed in the proposed algorithm switches between two different modes, namely, an individual walk on a single network and a simultaneous walk on two networks. The switching decision is made in a context-sensitive manner by examining the current neighborhood, which is effective for quantitatively estimating the degree of correspondence between nodes that belong to different networks, in a manner that sensibly integrates node similarity and topological similarity. The resulting node correspondence scores are then used to predict the maximum expected accuracy (MEA) alignment of the given networks.
  
  
  CONCLUSIONS
  Performance evaluation based on synthetic networks as well as real protein-protein interaction networks shows that the proposed algorithm can construct more accurate multiple network alignments compared to other leading methods.
authors.forename: Hyundoo
authors.initials: H
authors.lastname: Jeong
authors.forename: Byung-Jun
authors.initials: BJ
authors.lastname: Yoon
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D025941:Protein Interaction Mapping; D013269:Stochastic Processes