Exploitation of genetic interaction network topology for the prediction of epistatic behavior

- We perform prediction of epistasis based exclusively on network topology.
- Neighborhood-based prediction techniques are negatively impacted by network sparsity.
- Network embedding techniques tend to be more robust to network sparsity and noise.
- A technique based on penalization of low-degree nodes is proposed.
- The proposed technique predicts reliable interactions in dense and sparse networks.

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.