Nikolay Fabio, Pesavento Marius, Kritikos George, Typas Nassos
Communication Systems Group, TU Darmstadt, Merckstr. 25, Darmstadt, Germany.
European Molecular Biology Laboratory, Heidelberg, Meyerhofstraße 1, Heidelberg, 69117, Germany.
EURASIP J Bioinform Syst Biol. 2017 Sep 20;2017(1):10. doi: 10.1186/s13637-017-0063-3.
In this paper, we consider the problem of learning the genetic interaction map, i.e., the topology of a directed acyclic graph (DAG) of genetic interactions from noisy double-knockout (DK) data. Based on a set of well-established biological interaction models, we detect and classify the interactions between genes. We propose a novel linear integer optimization program called the Genetic-Interactions-Detector (GENIE) to identify the complex biological dependencies among genes and to compute the DAG topology that matches the DK measurements best. Furthermore, we extend the GENIE program by incorporating genetic interaction profile (GI-profile) data to further enhance the detection performance. In addition, we propose a sequential scalability technique for large sets of genes under study, in order to provide statistically significant results for real measurement data. Finally, we show via numeric simulations that the GENIE program and the GI-profile data extended GENIE (GI-GENIE) program clearly outperform the conventional techniques and present real data results for our proposed sequential scalability technique.
在本文中，我们考虑学习基因相互作用图谱的问题，即从有噪声的双基因敲除（DK）数据中学习基因相互作用的有向无环图（DAG）的拓扑结构。基于一组成熟的生物相互作用模型，我们检测并分类基因之间的相互作用。我们提出了一种名为基因相互作用检测器（GENIE）的新型线性整数优化程序，以识别基因之间复杂的生物依赖性，并计算与DK测量值最匹配的DAG拓扑结构。此外，我们通过纳入基因相互作用谱（GI-profile）数据来扩展GENIE程序，以进一步提高检测性能。此外，我们为正在研究的大量基因提出了一种顺序可扩展性技术，以便为实际测量数据提供具有统计显著性的结果。最后，我们通过数值模拟表明，GENIE程序和GI-profile数据扩展的GENIE（GI-GENIE）程序明显优于传统技术，并展示了我们提出的顺序可扩展性技术的实际数据结果。
