Department of Pharmacology and Pharmacotherapy, Semmelweis University, 1428 Budapest, Hungary.
Heart and Vascular Center, Semmelweis University, 1122 Budapest, Hungary.
Bioinformatics. 2019 Nov 1;35(21):4490-4492. doi: 10.1093/bioinformatics/btz257.
Network visualizations of complex biological datasets usually result in 'hairball' images, which do not discriminate network modules.
We present the EntOptLayout Cytoscape plug-in based on a recently developed network representation theory. The plug-in provides an efficient visualization of network modules, which represent major protein complexes in protein-protein interaction and signalling networks. Importantly, the tool gives a quality score of the network visualization by calculating the information loss between the input data and the visual representation showing a 3- to 25-fold improvement over conventional methods.
The plug-in (running on Windows, Linux, or Mac OS) and its tutorial (both in written and video forms) can be downloaded freely under the terms of the MIT license from: http://apps.cytoscape.org/apps/entoptlayout.
Supplementary data are available at Bioinformatics online.
复杂生物数据集的网络可视化通常会产生“毛发球”图像，无法区分网络模块。
我们提出了基于最近开发的网络表示理论的 EntOptLayout Cytoscape 插件。该插件提供了网络模块的有效可视化，这些模块代表蛋白质相互作用和信号网络中的主要蛋白质复合物。重要的是，该工具通过计算输入数据和视觉表示之间的信息丢失来为网络可视化提供质量评分，与传统方法相比，有 3 到 25 倍的改进。
该插件（在 Windows、Linux 或 Mac OS 上运行）及其教程（书面和视频形式）可根据麻省理工学院的许可条款免费下载：http://apps.cytoscape.org/apps/entoptlayout。
补充数据可在 Bioinformatics 在线获得。
