Dividing protein interaction networks for modular network comparative analysis
Pattern Recognition Letters
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
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: Recent advances in high-throughput experimental techniques have yielded a large amount of data on protein–protein interactions (PPIs). Since these interactions can be organized into networks, and since separate PPI networks can be constructed for different species, a natural research direction is the comparative analysis of such networks across species in order to detect conserved functional modules. This is the task of network alignment. Results: Most conventional network alignment algorithms adopt a node-then-edge-alignment paradigm: they first identify homologous proteins across networks and then consider interactions among them to construct network alignments. In this study, we propose an alternative direct-edge-alignment paradigm. Specifically, instead of explicit identification of homologous proteins, we directly infer plausibly alignable PPIs across species by comparing conservation of their constituent domain interactions. We apply our approach to detect conserved protein complexes in yeast–fly and yeast–worm PPI networks, and show that our approach outperforms two recent approaches in most alignment performance metrics. Availability: Supplementary material and source code can be found at http://www.cs.duke.edu/~amink/. Contact: xinguo@cs.duke.edu