Fast Approximate IsoRank for Scalable Global Alignment of Biological Networks

Kapil Devkota, Anselm Blumer, Xiaozhe Hu, Lenore Cowen

Abstract: The pioneering and still popular IsoRank method of Singh, Xu, and Berger for global alignment of two protein-protein interaction networks across species was introduced at Recomb in 2007, and was awarded the Recomb test of time award in 2019. However, with the availability of increasing amounts of experimental data the number of edges in the networks to align has grown considerably, making running IsoRank unfeasible on these networks without access to substantial computational resources. In this paper, we develop a new IsoRank approximation that exploits the mathematical properties of IsoRank’s linear system to solve the problem in quadratic time with respect to the maximum size of the two PPI networks. We further propose a refinement to this initial approximation so that the updated result is even closer to the original IsoRank formulation while remaining computationally inexpensive. In experiments on synthetic and real PPI networks with various proposed metrics to measure alignment quality, we find the results of our approximate IsoRank are nearly as accurate as the original IsoRank. In fact, for functional enrichment-based measures of global network alignment quality we find our approximation performs better than exact IsoRank, doubtless because it is more robust to the noise of missing or incorrect edges. It also performs competitively against two more recent global network alignment algorithms.

External IDs:dblp:conf/recomb/DevkotaBHC24