Domain-oriented edge-based alignment of protein interaction networks.
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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/ approximately amink/.
Protein Interaction Domains and Motifs
Protein Interaction Mapping
Published Version (Please cite this version)10.1093/bioinformatics/btp202
Publication InfoGuo, X; & Hartemink, Alexander J (2009). Domain-oriented edge-based alignment of protein interaction networks. Bioinformatics, 25(12). pp. i240-i246. 10.1093/bioinformatics/btp202. Retrieved from https://hdl.handle.net/10161/15156.
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Professor in the Department of Computer Science
Computational biology, machine learning, Bayesian statistics, systems biology, transcriptional regulation, genomics and epigenomics, graphical models, Bayesian networks, computational neurobiology, classification, feature selection