Cluster graph modification problems
Discrete Applied Mathematics - Discrete mathematics & data mining (DM & DM)
Finding occurrences of protein complexes in protein-protein interaction graphs
Journal of Discrete Algorithms
Topology-Free Querying of Protein Interaction Networks
RECOMB 2'09 Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology
Multiple Alignment of Biological Networks: A Flexible Approach
CPM '09 Proceedings of the 20th Annual Symposium on Combinatorial Pattern Matching
On perturbation theory and an algorithm for maximal clique enumeration in uncertain and noisy graphs
Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data
Aligning biomolecular networks using modular graph kernels
WABI'09 Proceedings of the 9th international conference on Algorithms in bioinformatics
Algorithms to detect multiprotein modularity conserved during evolution
ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
“Master-slave” biological network alignment
ISBRA'10 Proceedings of the 6th international conference on Bioinformatics Research and Applications
Discovery of extreme events-related communities in contrasting groups of physical system networks
Data Mining and Knowledge Discovery
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Comparative analysis of protein networks has proven to be a powerful approach for elucidating network structure and predicting protein function and interaction. A fundamental challenge for the successful application of this approach is to devise an efficient multiple network alignment algorithm. Here we present a novel framework for the problem. At the heart of the framework is a novel representation of multiple networks that is only linear in their size as opposed to current exponential representations. Our alignment algorithm is very efficient, being capable of aligning 10 networks with tens of thousands of proteins each in minutes. We show that our algorithm outperforms a previous strategy for the problem that is based on progressive alignment, and produces results that are more in line with current biological knowledge.