ArticlePloS one2012
A network synthesis model for generating protein interaction network families.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- An Experimental Study on the Scalability of Recent Node Centrality Metrics in Sparse Complex Networks.Frontiers in big data · 2022Article
- NAPAbench 2: A network synthesis algorithm for generating realistic protein-protein interaction (PPI) network families.PloS one · 2020Article
- A review of protein-protein interaction network alignment: From pathway comparison to global alignment.Computational and structural biotechnology journal · 2020Review
- Modelling Self-Organization in Complex Networks Via a Brain-Inspired Network Automata Theory Improves Link Reliability in Protein Interactomes.Scientific reports · 2018Article
- PrimAlign: PageRank-inspired Markovian alignment for large biological networks.Bioinformatics (Oxford, England) · 2018Article
- PROPER: global protein interaction network alignment through percolation matching.BMC bioinformatics · 2016Article
- Scope and limitations of yeast as a model organism for studying human tissue-specific pathways.BMC systems biology · 2015Article
- L-GRAAL: Lagrangian graphlet-based network aligner.Bioinformatics (Oxford, England) · 2015Article
- Accurate multiple network alignment through context-sensitive random walk.BMC systems biology · 2015Article
- GASOLINE: a Greedy And Stochastic algorithm for optimal Local multiple alignment of Interaction NEtworks.PloS one · 2014Article
- SMETANA: accurate and scalable algorithm for probabilistic alignment of large-scale biological networks.PloS one · 2013Article
Corrections and comments
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2 authors.
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Abstract
In this work, we introduce a novel network synthesis model that can generate families of evolutionarily related synthetic protein-protein interaction (PPI) networks. Given an ancestral network, the proposed model generates the network family according to a hypothetical phylogenetic tree, where the descendant networks are obtained through duplication and divergence of their ancestors, followed by network growth using network evolution models. We demonstrate that this network synthesis model can effectively create synthetic networks whose internal and cross-network properties closely resemble those of real PPI networks. The proposed model can serve as an effective framework for generating comprehensive benchmark datasets that can be used for reliable performance assessment of comparative network analysis algorithms. Using this model, we constructed a large-scale network alignment benchmark, called NAPAbench, and evaluated the performance of several representative network alignment algorithms. Our analysis clearly shows the relative performance of the leading network algorithms, with their respective advantages and disadvantages. The algorithm and source code of the network synthesis model and the network alignment benchmark NAPAbench are publicly available at http://www.ece.tamu.edu/bjyoon/NAPAbench/.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.