Combining biological networks to predict genetic interactions.
Genetic interactions define overlapping functions and compensatory pathways. In particular, synthetic sick or lethal (SSL) genetic interactions are important for understanding how an organism tolerates random mutation, i.e., genetic robustness. Comprehensive identification of SSL relationships remains far from complete in any organism, because mapping these networks is highly labor intensive. The ability to predict SSL interactions, however, could efficiently guide further SSL discovery. Toward this end, we predicted pairs of SSL genes in Saccharomyces cerevisiae by using probabilistic decision trees to integrate multiple types of data, including localization, mRNA expression, physical interaction, protein function, and characteristics of network topology. Experimental evidence demonstrated the reliability of this strategy, which, when extended to human SSL interactions, may prove valuable in discovering drug targets for cancer therapy and in identifying genes responsible for multigenic diseases.
Wong
Sharyl L
SL
Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 250 Longwood Avenue, Boston, MA 02115, USA.
Zhang
Lan V
LV
Tong
Amy H Y
AH
Li
Zhijian
Z
Goldberg
Debra S
DS
King
Oliver D
OD
Lesage
Guillaume
G
Vidal
Marc
M
Andrews
Brenda
B
Bussey
Howard
H
Boone
Charles
C
Roth
Frederick P
FP
D000818:Animals; D030541:Databases, Genetic; D003663:Decision Trees; D005838:Genotype; D008957:Models, Genetic; D015233:Models, Statistical; D009154:Mutation; D010641:Phenotype