Combining biological networks to predict genetic interactions.

@article{citeulike:106307, abstract = {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.}, address = {Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 250 Longwood Avenue, Boston, MA 02115, USA.}, author = {Wong, S. L. and Zhang, L. V. and Tong, A. H. and Li, Z. and Goldberg, D. S. and King, O. D. and Lesage, G. and Vidal, M. and Andrews, B. and Bussey, H. and Boone, C. and Roth, F. P. }, citeulike-article-id = {106307}, doi = {http://dx.doi.org/10.1073/pnas.0406614101}, issn = {0027-8424}, journal = {Proc Natl Acad Sci U S A}, keywords = {dataintegration, toread}, month = {November}, number = {44}, pages = {15682--15687}, posted-at = {2007-03-18 22:15:09}, priority = {3}, title = {Combining biological networks to predict genetic interactions.}, url = {http://dx.doi.org/10.1073/pnas.0406614101}, volume = {101}, year = {2004} }

TY - JOUR ID - citeulike:106307 L3 - citeulike-article-id:106307 N2 - 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. IS - 44 JF - Proc Natl Acad Sci U S A SN - 0027-8424 AD - Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 250 Longwood Avenue, Boston, MA 02115, USA. EP - 15687 TI - Combining biological networks to predict genetic interactions. VL - 101 SP - 15682 KW - dataintegration KW - toread AU - Wong, SL AU - Zhang, LV AU - Tong, AH AU - Li, Z AU - Goldberg, DS AU - King, OD AU - Lesage, G AU - Vidal, M AU - Andrews, B AU - Bussey, H AU - Boone, C AU - Roth, FP PY - 2004/11/02/ UR - http://dx.doi.org/10.1073/pnas.0406614101 ER -