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Our results were obtained using a bipartite network to model the spread of the disease.
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Tanay et al., 2004 also create one single network to analyze multiple genomic data at once; however, they use a bipartite network where each edge corresponds to one data type only.
We used a bipartite networks to construct the relationships of our data.
With the above datasets, we could construct a microRNA-microRNA network, a disease-disease network, and a microRNA-disease network using a bipartite graph.
We constructed the miRNA disease association network using a bipartite graph representation (see Figure 1) and analysed some statistics for the miRNA-disease association network.
Therefore, we constructed a sites-ligand interaction network using a bipartite graph to check the degree distributions of both binding sites and ligands.
We constructed the drug target interaction network for each protein class using a bipartite graph representation.
We constructed the drug target interaction network for each protein class using a bipartite graph representation (Yildirim et al., 2007).
According to the paradigm, the networks take care of managing topological information and forwarding decisions using a bipartite architecture in which a control plane decides the forwarding policies and the data plane (i.e. ordinary sensor nodes) executes them.
The use of a bipartite network is natural when dealing with two different types of data sets, in our case phenotypes and pathways.
Randomized version of the bipartite networks were built using the following algorithm: Given a bipartite network, we made a list of all the RG nodes, repeating each node as many times as the number on links incoming to it in the bipartite network.
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