Journal Article

Predicting pathway membership via domain signatures

Holger Fröhlich, Mark Fellmann, Holger Sültmann, Annemarie Poustka and Tim Beißbarth

in Bioinformatics

Volume 24, issue 19, pages 2137-2142
Published in print October 2008 | ISSN: 1367-4803
Published online August 2008 | e-ISSN: 1460-2059 | DOI:

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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.


Supplementary Information: Supplementary data are available at Bioinformatics online.

Journal Article.  3466 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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