Here, we present an approach using the vertices and edges labels of the given path ways. Consider two graphs, N1B and N2B, and a matrix representing the correspondences between V1 and V2. Let s and t be the origin and terminal nodes of edge e so the intersection of the path . An edge e? N1B is selected if both the originating and terminating vertices have corresponding vertices in N2B. To assess the importance of genes within each filtered pathway, we also implemented the betweenness central ity and degree centrality for each node. The degree and betweenness centrality of genes were calculated using the Reactome database as a base to cross validate our experimental results. The betweenness centrality of a node in a network topology measures how many shortest paths go through that node.

If bi is the ratio of the number of shortest paths between a pair of nodes in the network that pass through node i and the total number of shortest paths between those two nodes, the ways is defined as unscaled betweenness of node i is Bi all pairs bi, and the where v1?V1, v2?V2 and e1?E1, e2?E2. In other words, the intersection between all v1?V1 and v2?V2, and the inter section between corresponding edges e1?E1, e2?E2 under the condition that ? v1, v2 and ? e1, e2 betweenness centrality is where n is the number of nodes in the network. The betweenness centrality is positive and always less than or equal to 1 for any network. The degree of a node in a network is the number of connections or edges by which the node is related with other nodes.

Degree cen trality is the number of links that connect the node to the network divided by the number of nodes in the net work minus 1. It is a local measure that does not account for network context. However, changes in nodes with high degree centrality are likely to influence a large number of nodes in the network. The degree centrality was calculated by Formula. Formula indicates the degree centrality of an undirected graph. As for a vertex representing the gene in an undirected Dacomitinib graph, the higher the degree, the more reactions it interacts with and the more important the vertex is. Results and Discussion As described in previous section, we integrated the PID, KEGG and TRANSFAC public databases, and further eliminated duplicated reactions and elements.

Accord ingly, 8173 genes and 9308 interactions were retained, for which both detailed and summarized database results are presented in Table 2. In the next section, we present the experimental results and analysis of the pathway intersections. Significant pathways in ovarian cancer Ovarian cancer is among the most malignant of all lethal diseases in women. Currently, the preferred treatment regimen for ovarian cancer is combination chemotherapy primarily with platinum based drug such as cisplatin or carboplatin.