Gene function prediction from synthetic lethality networks via ranking on demand.
BACKGROUND
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.
Machine Learning & Computational Biology Research Group, Max Planck Institutes, Tübingen, Germany. christoph.lippert@tuebingen.mpg.de
Christoph
C
Lippert
Zoubin
Z
Ghahramani
Karsten M
KM
Borgwardt
D000465:Algorithms; D053263:Gene Regulatory Networks; D005804:Genes, Lethal; D016681:Genome, Fungal; D023281:Genomics / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000235:genetics; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics
D029701:Saccharomyces cerevisiae Proteins