Center for Advanced Studies, Research and Development (CRS4) Bioinformatica, Pula, Italy.
PLoS One. 2010 Oct 11;5(10):e12912. doi: 10.1371/journal.pone.0012912.
Reverse-engineering gene networks from expression profiles is a difficult problem for which a multitude of techniques have been developed over the last decade. The yearly organized DREAM challenges allow for a fair evaluation and unbiased comparison of these methods.
We propose an inference algorithm that combines confidence matrices, computed as the standard scores from single-gene knockout data, with the down-ranking of feed-forward edges. Substantial improvements on the predictions can be obtained after the execution of this second step.
Our algorithm was awarded the best overall performance at the DREAM4 In Silico 100-gene network sub-challenge, proving to be effective in inferring medium-size gene regulatory networks. This success demonstrates once again the decisive importance of gene expression data obtained after systematic gene perturbations and highlights the usefulness of graph analysis to increase the reliability of inference.
从表达谱中反向工程基因网络是一个难题，过去十年已经开发了许多技术。每年组织的 DREAM 挑战赛允许对这些方法进行公平的评估和无偏比较。
我们提出了一种推理算法，该算法将置信矩阵与前馈边缘的降序排列相结合，置信矩阵是从单个基因敲除数据的标准分数计算得出的。在执行第二步后，可以对预测进行实质性改进。
我们的算法在 DREAM4 模拟 100 个基因网络子挑战中获得了最佳整体性能，证明了它在推断中等大小基因调控网络方面的有效性。这一成功再次证明了系统基因扰动后获得的基因表达数据的决定性重要性，并强调了图分析在提高推理可靠性方面的有用性。
