PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Large Protein function prediction via faster graph kernels
Karsten Borgwardt, S V N Vishwanathan, Nicol Schraudolph and Hans-Peter Kriegel
In: NIPS workshop on Bio-informatics, 05 - 11 December 2005, Vancouver, Canada.

Abstract

Kernel functions on graphs have been defined over recent years. In earlier work, we have employed random walk graph kernels for predicting protein function from graph representations that integrate both protein sequence and structure. While yielding good protein function prediction results, random walk graph kernels suffer from a high computational complexity of $O(n^6)$ where $n$ is the number of nodes in the input graphs. In this paper, we present an approach for speeding up graph kernels. It is based on the observation that random walks on a graph can be regarded as a dynamical system and makes use of conjugate gradient.

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EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2046
Deposited By:S V N Vishwanathan
Deposited On:16 January 2006