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Bioinformatics. 2008 Oct 1;24(19):2137-42. doi: 10.1093/bioinformatics/btn403. Epub 2008 Aug 1.

Predicting pathway membership via domain signatures.

Author information

1
German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120 Heidelberg, Germany. h.froehlich@dkfz-heidelberg.de

Abstract

MOTIVATION:

Functional characterization of genes is of great importance for the understanding of complex cellular processes. Valuable information for this purpose can be obtained from pathway databases, like KEGG. However, only a small fraction of genes is annotated with pathway information up to now. In contrast, information on contained protein domains can be obtained for a significantly higher number of genes, e.g. from the InterPro database.

RESULTS:

We present a classification model, which for a specific gene of interest can predict the mapping to a KEGG pathway, based on its domain signature. The classifier makes explicit use of the hierarchical organization of pathways in the KEGG database. Furthermore, we take into account that a specific gene can be mapped to different pathways at the same time. The classification method produces a scoring of all possible mapping positions of the gene in the KEGG hierarchy. Evaluations of our model, which is a combination of a SVM and ranking perceptron approach, show a high prediction performance. Moreover, for signaling pathways we reveal that it is even possible to forecast accurately the membership to individual pathway components.

AVAILABILITY:

The R package gene2pathway is a supplement to this article.

PMID:
18676972
PMCID:
PMC2553439
DOI:
10.1093/bioinformatics/btn403
[Indexed for MEDLINE]
Free PMC Article

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