Alignment of dynamic networks
| Autor: | Tijana Milenkovic, Dominic Critchlow, Vipin Vijayan |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
0301 basic medicine Statistics and Probability Physics - Physics and Society Netbio Similarity (geometry) Dynamic network analysis Theoretical computer science Computer science Molecular Networks (q-bio.MN) 0206 medical engineering FOS: Physical sciences Physics and Society (physics.soc-ph) 02 engineering and technology Topology Models Biological Biochemistry Measure (mathematics) 03 medical and health sciences Yeasts Humans Quantitative Biology - Molecular Networks Protein Interaction Maps Molecular Biology Social and Information Networks (cs.SI) Node (networking) Computational Biology Social Support Computer Science - Social and Information Networks Ismb/Eccb 2017: The 25th Annual Conference Intelligent Systems for Molecular Biology Held Jointly with the 16th Annual European Conference on Computational Biology Prague Czech Republic July 21–25 2017 Computer Science Applications Computational Mathematics 030104 developmental biology Computational Theory and Mathematics FOS: Biological sciences Enhanced Data Rates for GSM Evolution Software 020602 bioinformatics |
| Zdroj: | Bioinformatics |
| ISSN: | 1367-4811 1367-4803 |
| Popis: | Motivation Network alignment (NA) aims to find a node mapping that conserves similar regions between compared networks. NA is applicable to many fields, including computational biology, where NA can guide the transfer of biological knowledge from well- to poorly-studied species across aligned network regions. Existing NA methods can only align static networks. However, most complex real-world systems evolve over time and should thus be modeled as dynamic networks. We hypothesize that aligning dynamic network representations of evolving systems will produce superior alignments compared to aligning the systems’ static network representations, as is currently done. Results For this purpose, we introduce the first ever dynamic NA method, DynaMAGNA ++. This proof-of-concept dynamic NA method is an extension of a state-of-the-art static NA method, MAGNA++. Even though both MAGNA++ and DynaMAGNA++ optimize edge as well as node conservation across the aligned networks, MAGNA++ conserves static edges and similarity between static node neighborhoods, while DynaMAGNA++ conserves dynamic edges (events) and similarity between evolving node neighborhoods. For this purpose, we introduce the first ever measure of dynamic edge conservation and rely on our recent measure of dynamic node conservation. Importantly, the two dynamic conservation measures can be optimized with any state-of-the-art NA method and not just MAGNA++. We confirm our hypothesis that dynamic NA is superior to static NA, on synthetic and real-world networks, in computational biology and social domains. DynaMAGNA++ is parallelized and has a user-friendly graphical interface. Availability and implementation http://nd.edu/∼cone/DynaMAGNA++/. Supplementary information Supplementary data are available at Bioinformatics online. |
| Databáze: | OpenAIRE |
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