MAGNA: Maximizing Accuracy in Global Network Alignment

TL;DR

MAGNA introduces a genetic algorithm-based method that directly optimizes edge conservation in biological network alignment, leading to improved accuracy over existing methods.

Contribution

It presents MAGNA, a novel genetic algorithm approach with a unique crossover function that directly optimizes alignment accuracy, unlike prior methods.

Findings

MAGNA outperforms existing methods like IsoRank, MI-GRAAL, and GHOST.

MAGNA improves alignment accuracy across various evaluations.

The method can optimize any alignment accuracy measure.

Abstract

Biological network alignment aims to identify similar regions between networks of different species. Existing methods compute node "similarities" to rapidly identify from possible alignments the "high-scoring" alignments with respect to the overall node similarity. However, the accuracy of the alignments is then evaluated with some other measure that is different than the node similarity used to construct the alignments. Typically, one measures the amount of conserved edges. Thus, the existing methods align similar nodes between networks hoping to conserve many edges (after the alignment is constructed!). Instead, we introduce MAGNA to directly "optimize" edge conservation while the alignment is constructed. MAGNA uses a genetic algorithm and our novel function for crossover of two "parent" alignments into a superior "child" alignment to simulate a "population" of alignments that "evolves" over time; the "fittest" alignments survive and proceed to the next "generation", until the alignment accuracy cannot be optimized further. While we optimize our new and superior measure of the amount of conserved edges, MAGNA can optimize any alignment accuracy measure. In systematic evaluations against existing state-of-the-art methods (IsoRank, MI-GRAAL, and GHOST), MAGNA improves alignment accuracy of all methods.