swMATH ID: 8318
Software Authors: Oleksii Kuchaiev; Natasa Przulj
Description: Integrative network alignment reveals large regions of global network similarity in yeast and human. Motivation: High-throughput methods for detecting molecular interactions have produced large sets of biological network data with much more yet to come. Analogous to sequence alignment, efficient and reliable network alignment methods are expected to improve our understanding of biological systems. Unlike sequence alignment, network alignment is computationally intractable. Hence, devising efficient network alignment heuristics is currently a foremost challenge in computational biology. Results: We introduce a novel network alignment algorithm, called Matching-based Integrative GRAph ALigner (MI-GRAAL), which can integrate any number and type of similarity measures between network nodes (e.g. proteins), including, but not limited to, any topological network similarity measure, sequence similarity, functional similarity and structural similarity. Hence, we resolve the ties in similarity measures and find a combination of similarity measures yielding the largest contiguous (i.e. connected) and biologically sound alignments. MI-GRAAL exposes the largest functional, connected regions of protein–protein interaction (PPI) network similarity to date: surprisingly, it reveals that 77.7
Homepage: http://bio-nets.doc.ic.ac.uk/MI-GRAAL/
Related Software: IsoRankN; IsoRank; Natalie; GHOST; NABEECO; SPINAL; C-GRAAL; GEDEVO
Cited in: 2 Documents

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