Fig. 1From: Discovering feature relevancy and dependency by kernel-guided probabilistic model-building evolutionDepiction of simultaneous variable selection and dependency estimation in data mining. a A dataset built with any data acquisition technology (here illustrated as a schematic high–throughput proteomic analysis of blood samples in a protein array using a mass spectrometer). b Marginal effects (independence) of the variables describing the data are modeled. c Interacting effects (dependencies) among variables are estimated, relevant variables are selected, whilst irrelevant variables along with associated dependencies are discarded (shown in grey)Back to article page