Figure 1. Feature selection path produced by our method on the MICE Protein Dataset.

Considering the cost of proteomic measurements, a trade-off between the number of features kept and statistical performance is often desirable. In this example, the method captures 70% of the signal with about 20% of the features. This allows to narrow down the list of important features, making the conclusions of the prediction task more actionable.