IsoRankN: spectral methods for global alignment of multiple protein networks
Name
Liao-2009-IsoRankN.pdf
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Author(s)
Liao, Chung-Shou
Lu, Kanghao
Baym, Michael Hartmann
Singh, Rohit
Date Issued
June 2009
Journal
Bioinformatics
Publisher
Oxford University Press
Citation
Liao, C.-S. et al. “IsoRankN: Spectral Methods for Global Alignment of Multiple Protein Networks.” Bioinformatics 25.12 (2009): i253–i258. Web.
Version
Final published version
Abstract
Motivation: With the increasing availability of large protein–protein interaction networks, the question of protein network alignment is becoming central to systems biology. Network alignment is further delineated into two sub-problems: local alignment, to find small conserved motifs across networks, and global alignment, which attempts to find a best mapping between all nodes of the two networks. In this article, our aim is to improve upon existing global alignment results. Better network alignment will enable, among other things, more accurate identification of functional orthologs across species.
Results: We introduce IsoRankN (IsoRank-Nibble) a global multiple-network alignment tool based on spectral clustering on the induced graph of pairwise alignment scores. IsoRankN outperforms existing algorithms for global network alignment in coverage and consistency on multiple alignments of the five available eukaryotic networks. Being based on spectral methods, IsoRankN is both error tolerant and computationally efficient.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Mathematics
Terms of Use
Creative Commons Attribution Non-Commercial
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1093/bioinformatics/btp203
