  Gene expression analysis aims at identifying the genes able to accurately
predict biological parameters like, for example, disease subtyping or
progression. While accurate prediction can be achieved by means of many
different techniques, gene identification, due to gene correlation and the
limited number of available samples, is a much more elusive problem. Small
changes in the expression values often produce different gene lists, and
solutions which are both sparse and stable are difficult to obtain. We propose
a two-stage regularization method able to learn linear models characterized by
a high prediction performance. By varying a suitable parameter these linear
models allow to trade sparsity for the inclusion of correlated genes and to
produce gene lists which are almost perfectly nested. Experimental results on
synthetic and microarray data confirm the interesting properties of the
proposed method and its potential as a starting point for further biological
investigations
