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 from Escherichia coli and Saccharomyces cerevisiae as 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 from E. coli and S. cerevisiae to 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.

基因调控网络（GRN）相互作用的表征为了解基因如何影响细胞表型提供了一个垫脚石。然而，尽管分析技术有所进步，但从基因表达数据重建GRN仍然是系统生物学中的紧迫问题。在这里，我们设计了一种监督学习方法GRADIS，该方法利用支持向量机基于从转录组学数据的图形表示中获得的距离轮廓来重建GRN。通过利用大肠杆菌和酿酒酵母的数据以及来自DREAM4的综合网络和五个网络推理挑战，我们证明了我们的GRADIS方法优于最新的有监督和无监督方法。当考虑到有关单个转录因子以及整个网络的靶基因的预测时，这一点成立。我们采用了来自大肠杆菌和酿酒酵母的经过实验验证的GRN，以验证预测并获得对所提出方法性能的进一步了解。我们的GRADIS方法为使用其他基于网络的大规模数据表示法提供了可能性，并且可以轻松扩展以帮助表征其他细胞网络，包括蛋白质-蛋白质和蛋白质-代谢物相互作用。