Tree-based learning of regulatory network topologies and dynamics with Jump3

Inference of gene regulatory networks (GRNs) from time series data is a well established field in computational systems biology. Most approaches can be broadly divided in two families: model-based and model-free methods. These two families are highly complementary: model-based methods seek to identify a formal mathematical model of the system. They thus have transparent and interpretable semantics, but rely on strong assumptions and are rather computationally intensive. On the other hand, model-free methods have typically good scalability. Since they are not based on any parametric model, they are more flexible that model-based methods, but also less interpretable. In this chapter, we describe Jump3, a hybrid approach that bridges the gap between model-free and model-based methods. Jump3 uses a formal stochastic differential equation to model each gene expression, but reconstructs the GRN topology with a non-parametric method based on decision trees. We briefly review the theoretical and algorithmic foundations of Jump3, and then proceed to provide a step by step tutorial of the associated software usage

Unsupervised gene network inference with decision trees and Random forests

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