Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Alignment of metabolic pathways
Bioinformatics
SAGA: a subgraph matching tool for biological graphs
Bioinformatics
Bioinformatics
Bioinformatics
QNet: a tool for querying protein interaction networks
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
Pairwise global alignment of protein interaction networks by matching neighborhood topology
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
Automatic parameter learning for multiple network alignment
RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
Querying Graphs in Protein-Protein Interactions Networks Using Feedback Vertex Set
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Top-k Similar Graph Matching Using TraM in Biological Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Restricted neighborhood search clustering revisited: an evolutionary computation perspective
PRIB'13 Proceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics
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Comparing and querying the protein-protein interaction (PPI) networks of different organisms is important to infer knowledge about conservation across species. Known methods that perform these tasks operate symmetrically, i.e., they do not assign a distinct role to the input PPI networks. However, in most cases, the input networks are indeed distinguishable on the basis of how the corresponding organism is biologically well characterized. In this paper a new idea is developed, that is, to exploit differences in the characterization of organisms at hand in order to devise methods for comparing their PPI networks. We use the PPI network (called Master) of the best characterized organism as a fingerprint to guide the alignment process to the second input network (called Slave), so that generated results preferably retain the structural characteristics of the Master network. Technically, this is obtained by generating from the Master a finite automaton, called alignment model, which is then fed with (a linearization of) the Slave for the purpose of extracting, via the Viterbi algorithm, matching subgraphs. We propose an approach able to perform global alignment and network querying, and we apply it on PPI networks. We tested our method showing that the results it returns are biologically relevant.