Bioinformatics Advance Access originally published online on August 1, 2008
Bioinformatics 2008 24(19):2137-2142; doi:10.1093/bioinformatics/btn403
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Predicting pathway membership via domain signatures
German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120 Heidelberg, Germany
*To whom correspondence should be addressed.
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Abstract |
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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.
Contact: h.froehlich{at}dkfz-heidelberg.de
Supplementary Information: Supplementary data are available at Bioinformatics online.
Associate Editor: Dmitrij Frishman
Received on April 23, 2008; revised on July 8, 2008; accepted on July 28, 2008
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