Revealing strengths and weaknesses of methods for gene network inference
Revealing strengths and weaknesses of methods for gene network inference
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DOI：
10.1073/pnas.0913357107
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发表时间：
2010-04-06
影响因子：
11.1
通讯作者：
Stolovitzky, Gustavo
中科院分区：
文献类型：
--
作者：
Marbach, Daniel;Prill, Robert J.;Stolovitzky, Gustavo
已经开发了许多方法来从表达数据推断基因调控网络，然而，它们的绝对性能和比较性能仍然知之甚少。在本文中，我们介绍了基因网络推理方法的关键性能评估框架。我们在DREAM（逆向工程评估和方法对话）项目的背景下提出了一个硅基准套件，作为一个盲法的、社区范围的挑战。我们评估了29种基因网络推断方法的性能，这些方法已被参与团队独立应用。性能分析表明，当前的推理方法在不同程度上受到不同类型的系统预测误差的影响。特别是，除了表现最好的方法外，所有方法都不能准确地推断基因的多重调节输入（组合调节）。这项社区范围内的实验结果表明，从基因表达数据中进行可靠的网络推断仍然是一个未解决的问题，它们表明了改进网络重建的潜在方法。
Numerous methods have been developed for inferring gene regulatory networks from expression data, however, both their absolute and comparative performance remain poorly understood. In this paper, we introduce a framework for critical performance assessment of methods for gene network inference. We present an in silico benchmark suite that we provided as a blinded, community-wide challenge within the context of the DREAM (Dialogue on Reverse Engineering Assessment and Methods) project. Weassess the performance of 29 gene-network-inference methods, which have been applied independently by participating teams. Performance profiling reveals that current inference methods are affected, to various degrees, by different types of systematic prediction errors. In particular, all but the best-performing method failed to accurately infer multiple regulatory inputs (combinatorial regulation) of genes. The results of this community-wide experiment show that reliable network inference from gene expression data remains an unsolved problem, and they indicate potential ways of network reconstruction improvements.