  This paper considers the task of learning how to make a prognosis of a
patient based on his/her micro-array expression levels. The method is an
application of the aggregation method as recently proposed in the literature on
theoretical machine learning, and excels in its computational convenience and
capability to deal with high-dimensional data. A formal analysis of the method
is given, yielding rates of convergence similar to what traditional techniques
obtain, while it is shown to cope well with an exponentially large set of
features. Those results are supported by numerical simulations on a range of
publicly available survival-micro-array datasets. It is empirically found that
the proposed technique combined with a recently proposed preprocessing
technique gives excellent performances.
