Chapter 2 A Panel of Learning Methods for the Reconstruction of Gene Regulatory Networks in a Systems Genetics Context

Abstract In this chapter, we study different gene regulatory network learning methods based on penalized linear regressions (the Lasso regression and the Dantzig Selector), Bayesian networks, and random forests. We also replicated the learning scheme using bootstrapped sub-samples of the observations. The biological motiva-tion relies on a tough nut to crack in Systems Biology: understanding the intertwined action of genome elements and gene activity to model gene regulatory features of an organism. We introduce the used methodologies, and then assess the methods on simulated “Systems Genetics ” (or genetical genomics) datasets. Our results show that methods have very different performances depending on tested simulation set-tings: total number of genes in the considered network, sample size, gene expression heritability, and chromosome length. We observe that the proposed approaches ar