Methods

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

    • 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
Published October 2, 2008. https://doi.org/10.1101/gr.077693.108
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cover of Genome Research Vol 36 Issue 6
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June 2026, Vol. 36, No. 6
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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.

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