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

机译：利用遗传相互作用网络拓扑预测上位行为

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摘要

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.
机译：遗传相互作用（GI）检测会影响对人类疾病的了解以及设计个性化治疗的能力。由于所需的基因缺失和敲低的组合数量，大多数生物体中每个GI的作图还远未完成。网络科学已经发展了仅基于网络拓扑来预测新交互的计算技术，但从未应用于GI网络。 GI的计算预测是辅助高吞吐量GI确定的强大工具。我们在本文中提出的差异指数能够获得精确的预测，从而减少要在实验室中测试的候选GI的范围。

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