Campus Event Calendar
Event Entry
What and Who
Learning kernels from biological networks by maximizing entropy
Koji Tsuda
University of Tübingen
Talk
AG 1, AG 2, AG 3, AG 4, AG 5
MPI Audience
Note: We use this to send email in the morning.
Date, Time and Location
Tuesday, 11 May 2004
16:00
-- Not specified --
46.1 - MPII
024
Saarbrücken
Abstract
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.
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.
Contact
Jörg Rahnenführer
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Ruth Schneppen-Christmann, 05/06/2004 10:48 -- Created document.