Dividing protein interaction networks for modular network comparative analysis
Pattern Recognition Letters
High efficiency and quality: large graphs matching
Proceedings of the 20th ACM international conference on Information and knowledge management
Scalable multiple global network alignment for biological data
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Prioritizing disease genes by bi-random walk
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Top-k Similar Graph Matching Using TraM in Biological Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
High efficiency and quality: large graphs matching
The VLDB Journal — The International Journal on Very Large Data Bases
Global Network Alignment In The Context Of Aging
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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Motivation: Aligning protein–protein interaction (PPI) networks of different species has drawn a considerable interest recently. This problem is important to investigate evolutionary conserved pathways or protein complexes across species, and to help in the identification of functional orthologs through the detection of conserved interactions. It is, however, a difficult combinatorial problem, for which only heuristic methods have been proposed so far. Results: We reformulate the PPI alignment as a graph matching problem, and investigate how state-of-the-art graph matching algorithms can be used for that purpose. We differentiate between two alignment problems, depending on whether strict constraints on protein matches are given, based on sequence similarity, or whether the goal is instead to find an optimal compromise between sequence similarity and interaction conservation in the alignment. We propose new methods for both cases, and assess their performance on the alignment of the yeast and fly PPI networks. The new methods consistently outperform state-of-the-art algorithms, retrieving in particular 78% more conserved interactions than IsoRank for a given level of sequence similarity. Availability: All data and codes are freely and publicly available upon request. Contact: jean-philippe.vert@mines-paristech.fr