Article: Employing functional interactions for characterisation and detection of sparse complexes from yeast PPI networks Journal: International Journal of Bioinformatics Research and Applications (IJBRA) 2012 Vol.8 No.3/4 pp.286 - 304 Abstract: Over the last few years, several computational techniques have been devised to recover protein complexes from the protein interaction (PPI) networks of organisms. These techniques model 'dense' subnetworks within PPI networks as complexes. However, our comprehensive evaluations revealed that these techniques fail to reconstruct many 'gold standard' complexes that are 'sparse' in the networks (only 71 recovered out of 123 known yeast complexes embedded in a network of 9704 interactions among 1622 proteins). In this work, we propose a novel index called Component-Edge (CE) score to quantitatively measure the notion of 'complex derivability' from PPI networks. Using this index, we theoretically categorise complexes as 'sparse' or 'dense' with respect to a given network. We then devise an algorithm SPARC that selectively employs functional interactions to improve the CE scores of predicted complexes, and thereby elevates many of the 'sparse' complexes to 'dense'. This empowers existing methods to detect these 'sparse' complexes. We demonstrate that our approach is effective in reconstructing significantly many complexes missed previously (104 recovered out of the 123 known complexes or ~47% improvement). Availability: http://www.comp.nus.edu.sg/leonghw/MCL-CAw/ Inderscience Publishers - linking academia, business and industry through research

Title: Employing functional interactions for characterisation and detection of sparse complexes from yeast PPI networks

Authors: Sriganesh Srihari; Hon Wai Leong

Addresses: Department of Computer Science, National University of Singapore, 117590 Singapore ' Department of Computer Science, National University of Singapore, 117590 Singapore

Abstract: Over the last few years, several computational techniques have been devised to recover protein complexes from the protein interaction (PPI) networks of organisms. These techniques model 'dense' subnetworks within PPI networks as complexes. However, our comprehensive evaluations revealed that these techniques fail to reconstruct many 'gold standard' complexes that are 'sparse' in the networks (only 71 recovered out of 123 known yeast complexes embedded in a network of 9704 interactions among 1622 proteins). In this work, we propose a novel index called Component-Edge (CE) score to quantitatively measure the notion of 'complex derivability' from PPI networks. Using this index, we theoretically categorise complexes as 'sparse' or 'dense' with respect to a given network. We then devise an algorithm SPARC that selectively employs functional interactions to improve the CE scores of predicted complexes, and thereby elevates many of the 'sparse' complexes to 'dense'. This empowers existing methods to detect these 'sparse' complexes. We demonstrate that our approach is effective in reconstructing significantly many complexes missed previously (104 recovered out of the 123 known complexes or ~47% improvement). Availability: http://www.comp.nus.edu.sg/leonghw/MCL-CAw/

Keywords: sparse complexes; complex prediction; protein interaction networks; functional interactions; protein–protein interactions; yeast PPIs; protein complexes; bioinformatics.

DOI: 10.1504/IJBRA.2012.048962

International Journal of Bioinformatics Research and Applications, 2012 Vol.8 No.3/4, pp.286 - 304

Published online: 05 Dec 2014 *

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Title: Employing functional interactions for characterisation and detection of sparse complexes from yeast PPI networks

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