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.
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.
Qi
Yan
Y
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA.
Suhail
Yasir
Y
Lin
Yu-yi
YY
Boeke
Jef D
JD
Bader
Joel S
JS
D000465:Algorithms; D015966:Gene Expression Regulation, Fungal; D005800:Genes, Fungal; D023281:Genomics / Q000379:methods*; D008957:Models, Genetic; D015233:Models, Statistical; D025941:Protein Interaction Mapping / Q000379:methods; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000235:genetics* / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics* / Q000378:metabolism
D029701:Saccharomyces cerevisiae Proteins