Downloaded from genome.cshlp.org on September 23, 2021 - Published by Cold Spring Harbor Laboratory Press 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 Joel S. Bader1,2,∗ 1Department of Biomedical Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA 2High-throughput Biology Center 3Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, 733 North Broadway, Baltimore, Maryland 21205, USA ∗To whom correspondence should be addressed,
[email protected] September 16, 2008 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. 1 Downloaded from genome.cshlp.org on September 23, 2021 - Published by Cold Spring Harbor Laboratory Press Background Two wrongs don’t make a right, but three rights make a left. — Anonymous Genetics establishes links between genotype and phenotype.