Graph kernels for disease outcome prediction from protein-protein interaction 
networks

Borgwardt, KM; Kriegel, H-P; Vishwanathan, SVN; Schraudolph, NN

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Conference Paper

Graph kernels for disease outcome prediction from protein-protein interaction networks

MPS-Authors

There are no MPG-Authors in the publication available

External Resource

http://psb.stanford.edu/psb-online/proceedings/psb07/borgwardt.pdf
(Publisher version)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Borgwardt, K., Kriegel, H.-P., Vishwanathan, S., & Schraudolph, N. (2007). Graph kernels for disease outcome prediction from protein-protein interaction networks. In R. Altman, A. Dunker, L. Hunter, T. Murray, & T. Klein (Eds.), Biocomputing 2007, Proceedings of the Pacific Symposium, Maui, Hawaii, USA, 3-7 January 2007 (pp. 4-15). Hackensack, NJ, USA: World Scientific.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-CEF9-5

Abstract

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.