Inferring Gene Regulatory Networks from high-throughput gene expression data is a challenging problem, addressed by the systems biology community. Most approaches that aim at unraveling the gene regulation mechanisms in a data-driven way, analyze gene expression datasets to score potential regulatory links between transcription factors and target genes. So far, three major families of approaches have been proposed to score regulatory links. These methods rely respectively on correlation measures, mutual information metrics, and regression algorithms. In this paper we present a new family of data-driven inference methods. This new family, inspired by the regression-based paradigm, relies on the use of classification algorithms. This paper assesses and advocates for the use of this paradigm as a new promising approach to infer gene regulatory networks. Indeed, the development and assessment of five new inference methods based on well-known classification algorithms shows that the classification-based inference family exhibits good results when compared to well-established paradigms.

中文翻译：

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基于分类算法的数据驱动基因调控网络推理

从高通量基因表达数据推断基因调控网络是一个具有挑战性的问题，由系统生物学界解决。大多数旨在以数据驱动的方式解开基因调控机制的方法，分析基因表达数据集以评估转录因子和靶基因之间的潜在调控联系。到目前为止，已经提出了三种主要的方法来对监管联系进行评分。这些方法分别依赖于相关性度量、互信息度量和回归算法。在本文中，我们提出了一系列新的数据驱动推理方法。这个受基于回归的范式启发的新系列依赖于分类算法的使用。本文评估并倡导使用这种范式作为推断基因调控网络的新方法。事实上，基于众所周知的分类算法的五种新推理方法的开发和评估表明，与成熟的范式相比，基于分类的推理系列表现出良好的结果。