SIRENE：调控网络的监督推理
SIRENE: Supervised Inference of Regulatory Networks
摘要：
活细胞是基因表达程序的产物，该程序涉及数千个基因的调节转录。因此，需要阐明转录调控网络来理解细胞的工作机制，并且可以例如用于发现新的治疗靶点。虽然已经提出了几种方法来推断基因调控网络从基因表达的数据，最近的一个大规模的基准实验的比较显示，目前大多数方法只能预测有限数量的已知法规在合理的精度水平。我们提出了SIRENE，一种新的方法，用于从表达数据的概要中推断基因调控网络。该方法将基因调控网络推理问题分解为大量的局部二进制分类问题，该问题集中于将每个TF的靶基因与非靶基因分离。因此，SIRENE在概念上简单且计算效率高。我们测试它的基准实验，旨在预测法规在E。大肠杆菌，并表明，它检索的顺序比其他国家的最先进的推理方法的6倍以上的已知规则。
英文摘要：
Living cells are the product of gene expression programs that involve the regulated transcription of thousands of genes. The elucidation of transcriptional regulatory networks in thus needed to understand the cell's working mechanism, and can for example be useful for the discovery of novel therapeutic targets. Although several methods have been proposed to infer gene regulatory networks from gene expression data, a recent comparison on a large-scale benchmark experiment revealed that most current methods only predict a limited number of known regulations at a reasonable precision level. We propose SIRENE, a new method for the inference of gene regulatory networks from a compendium of expression data. The method decomposes the problem of gene regulatory network inference into a large number of local binary classification problems, that focus on separating target genes from non-targets for each TF. SIRENE is thus conceptually simple and computationally efficient. We test it on a benchmark experiment aimed at predicting regulations in E. coli, and show that it retrieves of the order of 6 times more known regulations than other state-of-the-art inference methods.
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