A method for robust variable selection with significance assessment

Our goal is proposing an unbiased framework for gene expression analysis based on variable selection combined with a significance assessment step. We start by discussing the need of such a framework by illustrating the dramatic effect of a biased approach especially when the sample size is small. Then we describe our analysis protocol, based on two main ingredients. The first is a gene selection core based on elastic net regularization where we explicitly take into account regularization parameter tuning. The second is a general architecture to assess the statistical significance of the model via cross validation and permutation testing. Finally we challenge the system on real data experiments, and study its performance when changing variable selection algorithm or the dataset size.