Genetic programming researchers have shown a growing interest in the study of gene regulatory networks in the last few years. Our team has also contributed to the field, by defining two systems for the automatic reverse engineering of gene regulatory networks called GRNGen and GeNet. In this paper, we revise this work by describing in detail the two approaches and empirically comparing them. The results we report, and in particular the fact that GeNet can be used on large networks while GRNGen cannot, encourage us to pursue the study of GeNet in the future. We conclude the paper by discussing the main research directions that we are planning to investigate to improve GeNet.
['Leonardo Vanneschi', 'Matteo Mondini', 'Martino Bertoni', 'Alberto Ronchi', 'Mattia Stefano']
Gene regulatory networks reconstruction from time series datasets using genetic programming: a comparison between tree-based and graph-based approaches