IEEE/ACM Transactions on Computational Biology and Bioinformatics · 2015 · 97 citations · 77 references
Life SciencesBioinformaticsComputational BiologyNetwork BiologyProtein-protein InteractionsNetwork AlignmentPairwise AlignmentProtein-protein Interaction AnalysisProtein-protein Interaction NetworksBiological Network AlignmentEvaluation MetricsGlobal AlignmentGlobal Network AlignmentNetwork Alignment Evaluation MetricsPairwise Network AlignmentProtein Interaction Network AlignmentProtein-protein Interaction Network Alignment
Global network alignment maps nodes across multiple networks to identify optimal correspondences, a task increasingly important for graph‑based data such as protein–protein interaction networks that underpin many biological studies. This survey examines algorithms for global pairwise alignment of networks, categorizing them by methodology. The authors review alignment methods, evaluate them with common quality metrics, and summarize the most widely used protein‑interaction databases. Comparative analysis of selected aligners on identical datasets demonstrates relative performance differences under standardized metrics.
In this paper, we survey algorithms that perform global alignment of networks or graphs. Global network alignment aligns two or more given networks to find the best mapping from nodes in one network to nodes in other networks. Since graphs are a common method of data representation, graph alignment has become important with many significant applications. Protein-protein interactions can be modeled as networks and aligning these networks of protein interactions has many applications in biological research. In this survey, we review algorithms for global pairwise alignment highlighting various proposed approaches, and classify them based on their methodology. Evaluation metrics that are used to measure the quality of the resulting alignments are also surveyed. We discuss and present a comparison between selected aligners on the same datasets and evaluate using the same evaluation metrics. Finally, a quick overview of the most popular databases of protein interaction networks is presented focusing on datasets that have been used recently.
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