Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
Zur Ablage hinzufügen
  Lokale TagsFreigabegeschichteDetailsÜbersicht

Freigegeben
Konferenzbeitrag

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

MPG-Autoren
Es sind keine MPG-Autoren in der Publikation vorhanden
Externe Ressourcen

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

Volltexte (beschränkter Zugriff)
Für Ihren IP-Bereich sind aktuell keine Volltexte freigegeben.
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte in PuRe verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

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.


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