CUED Publications database

Gene function prediction from synthetic lethality networks via ranking on demand.

Lippert, C and Ghahramani, Z and Borgwardt, KM (2010) Gene function prediction from synthetic lethality networks via ranking on demand. Bioinformatics, 26. pp. 912-918.

Full text not available from this repository.


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.

Item Type: Article
Uncontrolled Keywords: Algorithms Gene Regulatory Networks Genes, Lethal Genome, Fungal Genomics Saccharomyces cerevisiae Saccharomyces cerevisiae Proteins
Divisions: Div F > Computational and Biological Learning
Depositing User: Unnamed user with email
Date Deposited: 16 Jul 2015 13:16
Last Modified: 28 Nov 2015 07:15
DOI: 10.1093/bioinformatics/btq053