PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Graph Kernels for Disease Outcome Prediction from Protein-Protein Interaction Networks
KM Borgwardt, HP Kriegel, S V N Vishwanathan and Nicol Schraudolph
In: Proceedings of the Pacific Symposium on Biocomputing (2007) World Scientific , Maui, Hawaii .


It is widely believed that comparing discrepancies in the protein-protein interaction (PPI) networks of individuals will become an important tool in understanding and preventing diseases. Currently PPI networks for individuals are not available, but gene expression data is becoming easier to obtain and allows us to represent individuals by a co-integrated gene expression/protein interaction network. Two major problems hamper the application of graph kernels – state-of-the-art methods for whole-graph comparison – to compare PPI networks. First, these methods do not scale to graphs of the size of a PPI network. Second, missing edges in these interaction networks are biologically relevant for detecting discrepancies, yet, these methods do not take this into account. In this article we present graph kernels for biological network comparison that are fast to compute and take into account missing interactions. We evaluate their practical performance on two datasets of co-integrated gene expression/PPI networks.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:3989
Deposited By:S V N Vishwanathan
Deposited On:25 February 2008