Multiple network alignment via multiMAGNA++

TL;DR

This paper introduces multiMAGNA++, a novel multiple network alignment method that optimizes both node and edge conservation, outperforming existing methods in accuracy and speed, and includes new quality measures for better evaluation.

Contribution

multiMAGNA++ is the first MNA method to directly optimize edge conservation during alignment, improving accuracy and scalability over existing approaches.

Findings

multiMAGNA++ outperforms existing MNA methods in accuracy.

multiMAGNA++ is faster and scales better to larger networks.

New MNA quality measures enable more comprehensive evaluation.

Abstract

Network alignment (NA) aims to find a node mapping between molecular networks of different species that identifies topologically or functionally similar network regions. Analogous to genomic sequence alignment, NA can be used to transfer biological knowledge from well- to poorly-studied species between aligned network regions. Pairwise NA (PNA) finds similar regions between two networks while multiple NA (MNA) can align more than two networks. We focus on MNA. Existing MNA methods aim to maximize total similarity over all aligned nodes (node conservation). Then, they evaluate alignment quality by measuring the amount of conserved edges, but only after the alignment is constructed. Directly optimizing edge conservation during alignment construction in addition to node conservation may result in superior alignments. Thus, we present a novel MNA approach called multiMAGNA++ that can achieve this. Indeed, multiMAGNA++ generally outperforms or is on par with the existing MNA methods, while often completing faster than the existing methods. That is, multiMAGNA++ scales well to larger network data and can be parallelized effectively. During method evaluation, we also introduce new MNA quality measures to allow for more complete alignment characterization as well as more fair MNA method comparison compared to using only the existing alignment quality measures.