Accurate multiple network alignment through context-sensitive random walk.

Byung-Jun Yoon,Hyundoo Jeong

doi:10.1186/1752-0509-9-s1-s7

Byung-Jun Yoon, Hyundoo Jeong

Open Access

https://doi.org/10.1186/1752-0509-9-s1-s7

Copy DOI

Abstract

BackgroundComparative 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.ResultsIn 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.ConclusionsPerformance 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.

Highlights

Comparative network analysis can provide an effective means of analyzing large-scale biological networks and gaining novel insights into their structure and organization
We propose a novel multiple network alignment algorithm based on a context-sensitive random walk (CSRW) model
We report the following performance metrics: correct nodes (CN), specificity (SPE), mean normalized entropy (MNE), conserved interaction (CI), coverage, and computation time

Summary

Introduction

Comparative network analysis can provide an effective means of analyzing large-scale biological networks and gaining novel insights into their structure and organization. Examination of conserved functional modules shows that many of the molecular interactions in such modules are well conserved, clearly showing the importance of taking the topological similarity into account when comparatively analyzing biological networks Biological networks, such as PPI networks, are typically represented as graphs, where the nodes represent individual biomolecules (e.g., proteins) and interactions (e.g., protein binding) between biomolecules are represented by edges connecting the corresponding nodes. Given these graph representations of biological networks, the network alignment problem can be formulated as an optimization problem whose goal is to find the optimal mapping - either one-to-one or many-to-many - among a set of graphs that maximizes a scoring function that assesses the goodness of a given mapping. As a result, existing network alignment methods employ various heuristic techniques to make the network alignment problem computationally tractable

Objectives

Methods

Results

Conclusion