Learning kernels from biological networks by maximizing
Koji Tsuda and William Stafford Noble
Bioinformatics (Proceedings of the ISMB/ECCB).
20(Suppl. 1):i326-i333, 2004.
The diffusion kernel is a general method for computing pairwise
distances among all nodes in a graph, based upon the sum of weighted
paths between each pair of nodes. This technique has been used
successfully, in conjunction with kernel-based learning methods, to
draw inferences from several types of biological networks. We show
that computing the diffusion kernel is equivalent to maximizing the
von Neumann entropy, subject to a global constraint on the sum of the
Euclidean distances between nodes. This global constraint allows for
high variance in the pairwise distances. Accordingly, we propose an
alternative, locally constrained diffusion kernel, and we demonstrate
that the resulting kernel allows for more accurate support vector
machine prediction of protein functional classifications from
metabolic and protein-protein interaction networks.