title: Exploitation of genetic interaction network topology for the prediction of epistatic behavior.
abstract: 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.
authors.affiliation: Integrative Systems Biology Lab, Biological and Environmental Sciences and Engineering Division, Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia; Division of Medical Genetics, Department of Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093 USA.
authors.forename: Gregorio
authors.initials: G
authors.lastname: Alanis-Lobato
authors.forename: Carlo Vittorio
authors.initials: CV
authors.lastname: Cannistraci
authors.forename: Timothy
authors.initials: T
authors.lastname: Ravasi
mesh_terms: D000465:Algorithms; D000818:Animals; D017173:Caenorhabditis elegans / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D004843:Epistasis, Genetic; D053263:Gene Regulatory Networks; D023281:Genomics; D006801:Humans; D012441:Saccharomyces cerevisiae / Q000235:genetics; D049490:Systems Biology
keywords: Computational biology; Gene networks; Genetic epistasis; Projections and predictions; Systems biology