Name
Title | ||
|---|---|---|
A network comparison algorithm for predicting the conservative interaction regions in protein-protein interaction network |
Abstract | ||
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We presented a network comparison algorithm for predicting the conservative interaction regions in the cross-species protein-protein interaction networks (PINs). In the first place, We made use of the correlated matrix to represent the PINs. Then we standardized the matrix and changed it into a unique representation to facilitate to judge whether the subgraphs is isomorphic. Then we proposed a network comparison algorithm based on the correlated matrix, edge-betweenness and the maximal frequent subgraphs mining. We used the tag grath library composed of the multiple PINs as input data and mined the maximal frequent subgraphs in the cross-species PINs by the algorithm. In the second place, we clustered and merged the similar but different and duplicate locally regions according to the similarity between them and the principle of sigle linkage clustering. In the end we analysed the resulting subgraphs and predicted the conservative interaction regions. The results showed the network comparison algorithm based on mining the frequent subgraplhs can be successfully applied to discover the conservative interaction regions, that is, we can find the functional complexes and predict the protein function. Furthermore, we can predict the interaction will exist in the other species when the conservative regions meet or exceed the threshold of minimum support. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/BICTA.2010.5645297 | BIC-TA |
Keywords | Field | DocType |
maximal frequent subgraphs mining,tag graph library,pattern clustering,network comparison,network comparison algorithm,the protein-protein interaction network,matrix algebra,the conservative interaction regions,correlated matrix,biology computing,conservative interaction regions,the correlated matrix standardization,cross-species protein-protein interaction networks,single linkage clustering,data mining,graph theory,mining the frequent subgraphs,edge-betweenness,pin,expert systems,machinery,correlation matrix,bioinformatics,proteins | Graph theory,Protein protein interaction network,Computer science,Matrix (mathematics),Matrix algebra,Algorithm,Isomorphism,Artificial intelligence,Protein function,Cluster analysis,Machine learning,Single-linkage clustering | Conference |
Volume | Issue | ISBN |
null | null | 978-1-4244-6437-1 |
Citations | PageRank | References |
5 | 0.45 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lihong Peng | 1 | 5 | 1.13 |
| Lipeng Liu | 2 | 8 | 1.46 |
| Shi Chen | 3 | 16 | 3.18 |
| Quanwei Sheng | 4 | 5 | 0.45 |