Huynh-Thu Vân Anh, Geurts Pierre
Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium.
Methods Mol Biol. 2019;1883:195-215. doi: 10.1007/978-1-4939-8882-2_8.
In this chapter, we introduce the reader to a popular family of machine learning algorithms, called decision trees. We then review several approaches based on decision trees that have been developed for the inference of gene regulatory networks (GRNs). Decision trees have indeed several nice properties that make them well-suited for tackling this problem: they are able to detect multivariate interacting effects between variables, are non-parametric, have good scalability, and have very few parameters. In particular, we describe in detail the GENIE3 algorithm, a state-of-the-art method for GRN inference.
在本章中，我们向读者介绍一类广受欢迎的机器学习算法——决策树。然后，我们回顾几种基于决策树为推断基因调控网络（GRN）而开发的方法。决策树确实具有一些优良特性，使其非常适合解决此问题：它们能够检测变量之间的多变量交互效应，是非参数的，具有良好的可扩展性，并且参数极少。特别是，我们详细描述了GENIE3算法，这是一种用于GRN推断的先进方法。
