Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.
Bioinformatics. 2010 Apr 15;26(8):1129-30. doi: 10.1093/bioinformatics/btq089. Epub 2010 Feb 25.
Increasing quantity and quality of data in transcriptomics and interactomics create the need for integrative approaches to network analysis. Here, we present a comprehensive R-package for the analysis of biological networks including an exact and a heuristic approach to identify functional modules.
The BioNet package provides an extensive framework for integrated network analysis in R. This includes the statistics for the integration of transcriptomic and functional data with biological networks, the scoring of nodes as well as methods for network search and visualization.
The BioNet package and a tutorial are available from http://bionet.bioapps.biozentrum.uni-wuerzburg.de.
转录组学和相互作用组学中数据数量和质量的增加，需要采用综合方法进行网络分析。在这里，我们提出了一个全面的 R 包，用于分析生物网络，包括一种精确和启发式的方法来识别功能模块。
BioNet 包为 R 中的综合网络分析提供了一个广泛的框架。这包括将转录组学和功能数据与生物网络集成的统计信息、节点评分以及网络搜索和可视化的方法。
BioNet 包和教程可从 http://bionet.bioapps.biozentrum.uni-wuerzburg.de 获取。
