Department of Biomedical Sciences and Engineering, University for Health Sciences, Medical Informatics and Technology (UMIT), Hall in Tirol, Austria.
Bioinformatics. 2011 Jan 1;27(1):140-1. doi: 10.1093/bioinformatics/btq606. Epub 2010 Nov 11.
Network-based representations of biological data have become an important way to analyze high-throughput data. To interpret the large amount of data that is produced by different high-throughput technologies, networks offer multifaceted aspects to analyze the data. As networks represent biological relationships within their structure, it turned out to be fruitful to analyze their topology. Therefore, we developed a freely available, open source R-package called Quantitative Analysis of Complex Networks (QuACN) to meet this challenge. QuACN contains different, information-theoretic and non-information-theoretic, topological network descriptors to analyze, classify and compare biological networks.
QuACN is freely available under LGPL via CRAN (http://cran.r-project.org/web/packages/QuACN/).
基于网络的生物数据表示已成为分析高通量数据的重要方法。为了解释不同高通量技术产生的大量数据，网络提供了多方面的角度来分析数据。由于网络在其结构中表示生物关系，因此分析它们的拓扑结构是富有成效的。因此，我们开发了一个免费的、开源的 R 包，称为复杂网络的定量分析（QuACN），以应对这一挑战。QuACN 包含不同的、信息论和非信息论的拓扑网络描述符，用于分析、分类和比较生物网络。
QuACN 可根据 LGPL 通过 CRAN（http://cran.r-project.org/web/packages/QuACN/）免费获得。
