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We only calculate the performance using the domainRBF approach based on diffusion kernel measure, and we choose the PDB part of the DOMINE database as the domain-domain interaction network.
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STMs are based on diffusion kernels (Kondor and Lafferty, 2002).
We have shown that generalized STMs can be obtained based on diffusion kernels.
Results: In this work, we derive generalized scale-aware versions of known graph topological measures based on diffusion kernels.
For our analysis, we used kernels from six heterogeneous data sets that have been used for supervised interactive network inference in a previous study [ 1]: three based on protein sequence kernels and three based on diffusion kernels.
Upper left panel shows a sensitivity kernel in 2D based on diffusion model.
A sensitivity kernel in 3D based on diffusion model and its projected plot are shown in lower left panel and lower right panel, respectively.
Our approach is presently based on diffusion theory calculations.
However, that work was based on diffusion theory [ 5].
sHKDR estimates the gene-gene similarities based on a diffusion kernel matrix, which is equivalent to a lazy random walker consisting of transitions from the current node to each of its neighbors with probability β and stay put with a probability of 1 - d i β (with d i as the degree of node i) [ 17].
A global method, based on graph diffusion kernels, predicts associations between enzymes that do not share metabolites.
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