Journal Article

Gene function prediction from synthetic lethality networks via ranking on demand

Christoph Lippert, Zoubin Ghahramani and Karsten M. Borgwardt

in Bioinformatics

Volume 26, issue 7, pages 912-918
Published in print April 2010 | ISSN: 1367-4803
Published online February 2010 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btq053
Gene function prediction from synthetic lethality networks via ranking on demand

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Motivation: Synthetic lethal interactions represent pairs of genes whose individual mutations are not lethal, while the double mutation of both genes does incur lethality. Several studies have shown a correlation between functional similarity of genes and their distances in networks based on synthetic lethal interactions. However, there is a lack of algorithms for predicting gene function from synthetic lethality interaction networks.

Results: In this article, we present a novel technique called kernelROD for gene function prediction from synthetic lethal interaction networks based on kernel machines. We apply our novel algorithm to Gene Ontology functional annotation prediction in yeast. Our experiments show that our method leads to improved gene function prediction compared with state-of-the-art competitors and that combining genetic and congruence networks leads to a further improvement in prediction accuracy.

Contact: christoph.lippert@tuebingen.mpg.de

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  4688 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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