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摘要
We presented a network comparison method for finding the conservative interaction regions in the across-species protein interaction networks (PINs). In the first part, firstly, 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. Subsequently, we proposed a network comparison algorithm based on the correlated matrix, edge-betweenness and the maximal frequent subgraphs mining. We used the tag graphs library composed of the multiple across-species PINs as input data and mined the maximal frequent subgraphs. In the second part, we clustered and merged the similar but different and duplicate locally regions according to the similarity between them and the principle of single 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 subgraphs can be successfully applied to discover the conservative interaction regions, that is to say, we can find the functional complexes. Furthermore, we can conclude the protein interactions existing in one species will be able to exist in the other species when the occurrences of the conservative regions meet or exceed the threshold of minimum support.
