A Novel Computational Approach for Global Alignment for Multiple Biological Networks
Résumé
—Due to the rapid progress of biological networks for modeling biological systems, a lot of biomolecular networks have been producing more and more protein-protein interaction (PPI) data. Analyzing protein-protein interaction (PPI) networks aims to find regions of topological and functional (dis)similarities between molecular networks of different species. The study of PPI networks has the potential to teach us as much about life process and diseases at the molecular level. Although few methods have been developed for multiple PPI network alignment, the alignment quality is still far away from being outstanding and thus, new network alignment methods are of a compelling need. In this paper, we propose a novel algorithm for a global alignment of multiple protein-protein interaction (PPI) networks called MAPPIN, which combines information available for the proteins in the networks, including sequence, function and network topology. Our method is perfectly designed to exploit current multi-core CPU architectures. The method has been extensively tested on a real data (five eukaryotic species). Our experimental results show that MAPPIN outperforms several popular methods in producing functionally coherent alignments as it provides biologically significant alignments within an acceptable running time, even for very large input instances.
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