title: 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.
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.
authors.affiliation: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA.
authors.forename: Yan
authors.initials: Y
authors.lastname: Qi
authors.forename: Yasir
authors.initials: Y
authors.lastname: Suhail
authors.forename: Yu-yi
authors.initials: YY
authors.lastname: Lin
authors.forename: Jef D
authors.initials: JD
authors.lastname: Boeke
authors.forename: Joel S
authors.initials: JS
authors.lastname: Bader
mesh_terms: 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
chemical_list: D029701:Saccharomyces cerevisiae Proteins