Gene Regulatory Network Inference from Systems Genetics Data Using Tree-Based Methods

One of the pressing open problems of computational systems biology is the elucidation of the topology of gene regulatory networks (GRNs). In an attempt to solve this problem, the idea of systems genetics is to exploit the natural variations that exist between the DNA sequences of related individuals and that can represent the randomized and multifactorial perturbations necessary to recover GRNs. In this chapter, we present new methods, called GENIE3-SG-joint and GENIE3-SG-sep, for the inference of GRNs from systems genetics data. Experiments on the artificial data of the StatSeq benchmark and of the DREAM5 Systems Genetics challenge show that exploiting jointly expression and genetic data is very helpful for recovering GRNs, and one of our methods outperforms by a large extent the official best performing method of the DREAM5 challenge.

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