Bellot Pau, Olsen Catharina, Salembier Philippe, Oliveras-Vergés Albert, Meyer Patrick E
Universitat Politecnica de Catalunya BarcelonaTECH, Department of Signal Theory and Communications, UPC-Campus Nord, C/ Jordi Girona, 1-3, Barcelona, 08034, Spain.
Bioinformatics and Systems Biology (BioSys), Faculty of Sciences, Université de Liège (ULg), 27 Blvd du Rectorat, Liège, 4000, Belgium.
BMC Bioinformatics. 2015 Sep 29;16:312. doi: 10.1186/s12859-015-0728-4.
In the last decade, a great number of methods for reconstructing gene regulatory networks from expression data have been proposed. However, very few tools and datasets allow to evaluate accurately and reproducibly those methods. Hence, we propose here a new tool, able to perform a systematic, yet fully reproducible, evaluation of transcriptional network inference methods.
Our open-source and freely available Bioconductor package aggregates a large set of tools to assess the robustness of network inference algorithms against different simulators, topologies, sample sizes and noise intensities.
The benchmarking framework that uses various datasets highlights the specialization of some methods toward network types and data. As a result, it is possible to identify the techniques that have broad overall performances.
在过去十年中，已经提出了大量从表达数据重建基因调控网络的方法。然而，很少有工具和数据集能够准确且可重复地评估这些方法。因此，我们在此提出一种新工具，能够对转录网络推断方法进行系统且完全可重复的评估。
我们的开源且免费可用的Bioconductor软件包聚合了大量工具，以评估网络推断算法针对不同模拟器、拓扑结构、样本大小和噪声强度的稳健性。
使用各种数据集的基准测试框架突出了一些方法针对网络类型和数据的特殊性。因此，有可能识别出具有广泛总体性能的技术。
