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

Borgwardt, KM; Vishwanathan SVN, Schraudolph, N; Kriegel, H-P; Altman,; Dunker, R.B. A.K.; Hunter, L.; Murray, T.; Klein, T.E.

アイテム詳細

登録内容を編集ファイル形式で保存

一時保存へ追加

このアイテムの新しいバージョンが利用可能です:
https://pure.mpg.de/pubman/item/item_1790644_3

詳細要約

公開

会議論文

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

MPS-Authors

/persons/resource/persons75313

Borgwardt, KM
Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

There are no locators available

Fulltext (restricted access)

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

フルテキスト (公開)

公開されているフルテキストはありません

付随資料 (公開)

There is no public supplementary material available

引用

Borgwardt, K., Vishwanathan SVN, Schraudolph, N., & Kriegel, H.-P. (2007). Graph kernels for disease outcome prediction from protein-protein interaction networks. In Pacific Symposium on Biocomputing (PSB 2007) (pp. 4-15). Hackensack, NJ, USA: World Scientific.

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

要旨

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.