Nets Versus Trees for Feature Ranking and Gene Network Inference

Abstract

We propose to tackle the challenging problem of gene regulatory network inference, using variable importance measures derived from artificial neural networks (ANN). When combined with a L1-regularized selection layer, these measures allow ANN to be competitive with state of the art techniques for this problem based on random forests.