NetCoffee: a fast and accurate global alignment approach to identify functionally conserved proteins in multiple networks.
Jialu Hu,Birte Kehr,Knut Reinert +2 more
TL;DR: This work presents a fast and accurate algorithm, NetCoffee, which allows to find a global alignment of multiple protein-protein interaction networks and suggests that it outperforms all existing alignment tools in terms of speed and nevertheless identifies biologically meaningful alignments.
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Abstract: Motivation: Owing to recent advancements in high-throughput technologies, protein–protein interaction networks of more and more species become available in public databases. The question of how to identify functionally conserved proteins across species attracts a lot of attention in computational biology. Network alignments provide a systematic way to solve this problem. However, most existing alignment tools encounter limitations in tackling this problem. Therefore, the demand for faster and more efficient alignment tools is growing. Results: We present a fast and accurate algorithm, NetCoffee ,w hich allows to find a global alignment of multiple protein–protein interaction networks. NetCoffee searches for a global alignment by maximizing a target function using simulated annealing on a set of weighted bipartite graphs that are constructed using a triplet approach similar to T-Coffee. To assess its performance, NetCoffee was applied to four real datasets. Our results suggest that NetCoffee remedies several limitations of previous algorithms, outperforms all existing alignment tools in terms of speed and nevertheless identifies biologically meaningful alignments. Availability: The source code and data are freely available for download under the GNU GPL v3 license at https://code.google.com/p/ netcoffee/.
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Citations
SANA: Simulated Annealing Network Alignment Applied to Biological Networks
Nil Mamano,Wayne Hayes +1 more
TL;DR: This paper introduces SANA, a stochastic search algorithm for network alignment, which significantly outperforms existing methods on various objective functions, including biological and topological measures, and is available as open-source software.
Node Handprinting: A Scalable and Accurate Algorithm for Aligning Multiple Biological Networks.
Alex Radu,Michael A. Charleston +1 more
TL;DR: A fast and accurate algorithmic approach, Node Handprinting (NH), based on previous work with Node Fingerprinting, which enables quick and accurate alignment of multiple networks and is the only algorithm capable of performing the more complex alignments.
A Simulated Annealing Algorithm for Maximum Common Edge Subgraph Detection in Biological Networks
Simon J. Larsen,Frederik G. Alkærsig,Henrik J. Ditzel,Igor Jurisica,Nicolas Alcaraz,Jan Baumbach +5 more
- 20 Jul 2016
TL;DR: A heuristic algorithm is introduced for the multiple maximum common edge subgraph problem that is able to detect large common substructures shared across multiple, real-world size networks efficiently and is parallelized and well-suited to exploit current multi-core CPU architectures.
Exact P-Values for Global Network Alignments Via Combinatorial Analysis of Shared GO Terms
Wayne B. Hayes
TL;DR: Researchers propose a combinatorial method to calculate exact p-values for global network alignments, evaluating the statistical significance of shared GO terms and providing independent feedback on alignment quality, correlating with GO annotation prediction precision.
Protein2Vec: Aligning Multiple PPI Networks with Representation Learning
TL;DR: The topological similarity of proteins from different PPI networks can be transferred as the similarity of their corresponding vector representations, which provides a new way to comprehensively quantify the topological similarities between proteins.
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