PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Gene function prediction from synthetic lethality networks via ranking on demand
Christoph Lippert, Zoubin Ghahramani and Karsten Borgwardt
Bioinformatics Volume 26, Number 7, pp. 912-918, 2010.

Abstract

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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:7818
Deposited By:Zoubin Ghahramani
Deposited On:17 March 2011