Exploitation of genetic interaction network topology for the prediction of epistatic behavior
Exploitation of genetic interaction network topology for the prediction of epistatic behavior
复制标题
DOI：
10.1016/j.ygeno.2013.07.010
复制
发表时间：
2013-10-01
期刊：
影响因子：
4.4
通讯作者：
Ravasi, Timothy
中科院分区：
文献类型：
--
作者：
Alanis-Lobato, Gregorio;Cannistraci, Carlo Vittorio;Ravasi, Timothy
遗传相互作用（GI）检测影响对人类疾病的理解和设计个性化治疗的能力。由于所需的基因缺失和敲除的组合量，大多数生物体中每个GI的映射远未完成。仅基于网络拓扑预测新交互的计算技术在网络科学中得到了发展，但从未应用于GI网络.我们证明了GI的拓扑预测是可能的，具有高精度，并提出了一个图相异度指数，能够在密集和稀疏网络中提供鲁棒的预测. GI的计算预测是一个强大的工具，以帮助高吞吐量GI确定.我们在这篇文章中提出的相异度指数能够获得精确的预测，减少在实验室测试的候选地理信息系统的宇宙。(C)2013 Elsevier Inc. All rights reserved.
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. (C) 2013 Elsevier Inc. All rights reserved.