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FUSE: Multiple Network Alignment via Data Fusion by Gligorijević, Vladimir; Malod-Dognin, Noël; Pržulj, Nataša is a scholarly article available to read on EtoBox.

What is FUSE: Multiple Network Alignment via Data Fusion about?

Discovering patterns in networks of protein-protein interactions (PPIs) is a central problem in systems biology. Alignments between these networks aid functional understanding as they uncover important information, such as evolutionary conserved pathways, protein complexes and functional orthologs. The objective of a multiple network alignment is to create clusters of nodes that are evolutionarily conserved and functionally consistent across all networks. Unfortunately, the alignment methods proposed thus far do not fully meet this objective, as they are guided by pairwise scores that do not utilize the entire functional and topological information across all networks. To overcome this weakness, we propose FUSE, a multiple network aligner that utilizes all functional and topological information in all PPI networks. It works in two steps. First, it computes novel similarity scores of proteins across the PPI networks by fusing from all aligned networks both the protein wiring patterns and their sequence similarities. It does this by using Non-negative Matrix Tri-Factorization (NMTF). When we apply NMTF on the five largest and most complete PPI networks from BioGRID, we show that NMTF

Author

Gligorijević, Vladimir; Malod-Dognin, Noël; Pržulj, Nataša

Published

2014

Language

EN