TY - JOUR
T1 - Exploitation of genetic interaction network topology for the prediction of epistatic behavior
AU - Alanis Lobato, Gregorio
AU - Cannistraci, Carlo
AU - Ravasi, Timothy
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2013/10
Y1 - 2013/10
N2 - Genetic interaction (GI) detection impacts the understanding of human disease and the ability to design personalized treatment. The mapping of every GI in most organisms is far from complete due to the combinatorial amount of gene deletions and knockdowns required. Computational techniques to predict new interactions based only on network topology have been developed in network science but never applied to GI networks.We show that topological prediction of GIs is possible with high precision and propose a graph dissimilarity index that is able to provide robust prediction in both dense and sparse networks.Computational prediction of GIs is a strong tool to aid high-throughput GI determination. The dissimilarity index we propose in this article is able to attain precise predictions that reduce the universe of candidate GIs to test in the lab. © 2013 Elsevier Inc.
AB - Genetic interaction (GI) detection impacts the understanding of human disease and the ability to design personalized treatment. The mapping of every GI in most organisms is far from complete due to the combinatorial amount of gene deletions and knockdowns required. Computational techniques to predict new interactions based only on network topology have been developed in network science but never applied to GI networks.We show that topological prediction of GIs is possible with high precision and propose a graph dissimilarity index that is able to provide robust prediction in both dense and sparse networks.Computational prediction of GIs is a strong tool to aid high-throughput GI determination. The dissimilarity index we propose in this article is able to attain precise predictions that reduce the universe of candidate GIs to test in the lab. © 2013 Elsevier Inc.
UR - http://hdl.handle.net/10754/563027
UR - https://linkinghub.elsevier.com/retrieve/pii/S088875431300147X
UR - http://www.scopus.com/inward/record.url?scp=84886241235&partnerID=8YFLogxK
U2 - 10.1016/j.ygeno.2013.07.010
DO - 10.1016/j.ygeno.2013.07.010
M3 - Article
C2 - 23892246
SN - 0888-7543
VL - 102
SP - 202
EP - 208
JO - Genomics
JF - Genomics
IS - 4
ER -