Combining tree-based and dynamical systems for the inference of gene regulatory networks

Abstract

Here, we propose a new hybrid approach for GRN inference, called Jump3, exploiting time series of expression data. Jump3 is based on a formal on/off model of gene expression but uses a non-parametric procedure based on decision trees (called 'jump trees') to reconstruct the GRN topology, allowing the inference of networks of hundreds of genes. We show the good performance of Jump3 on in silico and synthetic networks and applied the approach to identify regulatory interactions activated in the presence of interferon gamma.