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. co...

• Type: Journal
• Year: 2011
• Where: BIOINFORMATICS
• Authors: Oleksii Kuchaiev, Natasa Przulj
• Keywords: Artificial Intelligence, BIOINFORMATICS 2011, Network Alignment, PPI Networks, Similarity Measures