Supervised learning of gene-regulatory networks based on graph distance profiles of transcriptomics data
Supervised learning of gene-regulatory networks based on graph distance profiles of transcriptomics data
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DOI：
10.1038/s41540-020-0140-1
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发表时间：
2020-06-30
影响因子：
4
通讯作者：
Nikoloski, Zoran
中科院分区：
文献类型：
--
作者：
Razaghi-Moghadam, Zahra;Nikoloski, Zoran
基因调控网络(GRN)相互作用的特征为理解基因如何影响细胞表型提供了踏脚石。然而，尽管图谱技术取得了进步，但从基因表达数据重建GRN仍然是系统生物学中的一个紧迫问题。在这里，我们设计了一种有监督的学习方法Gradis，它利用支持向量机根据从转录数据的图形表示获得的距离轮廓来重建GRN。通过使用来自大肠杆菌和酿酒酵母的数据，以及来自DREAM4和五个网络推理挑战的合成网络，我们证明了我们的Gradis方法比最先进的监督和非监督方法要好。当考虑到对单个转录因子以及整个网络的目标基因的预测时，这一点是成立的。我们采用了经过实验验证的GRN。科兰德·S。验证预测，并进一步了解拟议方法的执行情况。我们的Gradis方法提供了使用其他基于网络的大规模数据表示的可能性，并且可以很容易地扩展以帮助描述其他细胞网络的特征，包括蛋白质-蛋白质和蛋白质-代谢物相互作用。
Characterisation of gene-regulatory network (GRN) interactions provides a stepping stone to understanding how genes affect cellular phenotypes. Yet, despite advances in profiling technologies, GRN reconstruction from gene expression data remains a pressing problem in systems biology. Here, we devise a supervised learning approach, GRADIS, which utilises support vector machine to reconstruct GRNs based on distance profiles obtained from a graph representation of transcriptomics data. By employing the data fromEscherichia coliandSaccharomyces cerevisiaeas well as synthetic networks from the DREAM4 and five network inference challenges, we demonstrate that our GRADIS approach outperforms the state-of-the-art supervised and unsupervided approaches. This holds when predictions about target genes for individual transcription factors as well as for the entire network are considered. We employ experimentally verified GRNs fromE. coliandS. cerevisiaeto validate the predictions and obtain further insights in the performance of the proposed approach. Our GRADIS approach offers the possibility for usage of other network-based representations of large-scale data, and can be readily extended to help the characterisation of other cellular networks, including protein-protein and protein-metabolite interactions.