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Gene function prediction from synthetic lethality networks via ranking on demand




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.

Author(s): Lippert, C. and Ghahramani, Z. and Borgwardt, KM.
Journal: Bioinformatics
Volume: 26
Number (issue): 7
Pages: 912-918
Year: 2010
Month: April
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1093/bioinformatics/btq053
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web


  title = {Gene function prediction from synthetic lethality networks via ranking on demand},
  author = {Lippert, C. and Ghahramani, Z. and Borgwardt, KM.},
  journal = {Bioinformatics},
  volume = {26},
  number = {7},
  pages = {912-918},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = apr,
  year = {2010},
  month_numeric = {4}