Modeling gene expression data via positive Boolean functions
In this work we propose an artificial model for the generation of biologically plausible gene expression data to be used in the evaluation of the performance of gene selection and clustering methods. The model, allows to fix in advance the set of relevant genes and the functional classes involved in the problem; the input-output relationship is constructed by synthesizing a positive Boolean function. Despite its simplicity, it is sufficiently rich to take account of the specific peculiarities of gene expression, including the biological variability. As an applicative example, we also provide some data simulations and numerical experiments for the analysis of the performances of gene selection methods.