2011/08/22 by Mohammed El-Kebir, El-Kebir, Mohammed, Jaap Heringa +3
Biochemistry, Genetics and Molecular Biology · Computer Science · #Bioinformatics and Genomic Networks #Computational Drug Discovery Methods #Data Structures and Algorithms (cs.DS) #FOS: Biological sciences #FOS: Computer and information sciences #Gene Regulatory Network Analysis #Quantitative Methods (q-bio.QM) #cs.DS #q-bio.QM
paper · pdf · doi:10.48550/arxiv.1108.4358
arxiv created 2011/08/22 · openalex publication_date 2011/08/22 · arxiv updated 2011/08/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Data on molecular interactions is increasing at a tremendous pace, while the development of solid methods for analyzing this network data is lagging behind. This holds in particular for the field of comparative network analysis, where one wants to identify commonalities between biological networks. Since biological functionality primarily operates at the network level, there is a clear need for topology-aware comparison methods. In this paper we present a method for global network alignment that is fast and robust, and can flexibly deal with various scoring schemes taking both node-to-node correspondences as well as network topologies into account. It is based on an integer linear programming formulation, generalizing the well-studied quadratic assignment problem. We obtain strong upper and lower bounds for the problem by improving a Lagrangian relaxation approach and introduce the software tool natalie 2.0, a publicly available implementation of our method. In an extensive computational study on protein interaction networks for six different species, we find that our new method outperforms alternative state-of-the-art methods with respect to quality and running time.