Finding friends and enemies in an enemies-only network: A graph diffusion kernel for predicting novel genetic interactions and co-complex membership from yeast genetic interactions
- Yan Qi1,
- Yasir Suhail1,
- Yu-yi Lin2,3,
- Jef D. Boeke2,3, and
- Joel S. Bader1,2,4
- 1 Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA;
- 2 High-Throughput Biology Center, Johns Hopkins University School of Medicine, Baltimore, Maryland 21218, USA;
- 3 Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
Abstract
The yeast synthetic lethal genetic interaction network contains rich information about underlying pathways and protein complexes as well as new genetic interactions yet to be discovered. We have developed a graph diffusion kernel as a unified framework for inferring complex/pathway membership analogous to “friends” and genetic interactions analogous to “enemies” from the genetic interaction network. When applied to the Saccharomyces cerevisiae synthetic lethal genetic interaction network, we can achieve a precision around 50% with 20% to 50% recall in the genome-wide prediction of new genetic interactions, supported by experimental validation. The kernels show significant improvement over previous best methods for predicting genetic interactions and protein co-complex membership from genetic interaction data.
Footnotes
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↵4 Corresponding author.
↵4 E-mail joel.bader{at}jhu.edu; fax (410) 516-5294.
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[Supplemental material is available online at www.genome.org.]
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Article published online before print. Article and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.077693.108.
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- Received March 11, 2008.
- Accepted September 18, 2008.
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Freely available online through the Genome Research Open Access option.
- Copyright © 2008, Cold Spring Harbor Laboratory Press
