  Motivation: Array-based comparative genomic hybridization (arrayCGH) has
recently become a popular tool to identify DNA copy number variations along the
genome. These profiles are starting to be used as markers to improve prognosis
or diagnosis of cancer, which implies that methods for automated supervised
classification of arrayCGH data are needed. Like gene expression profiles,
arrayCGH profiles are characterized by a large number of variables usually
measured on a limited number of samples. However, arrayCGH profiles have a
particular structure of correlations between variables, due to the spatial
organization of BACs along the genome. This suggests that classical
classification methods, often based on the selection of a small number of
discriminative features, may not be the most accurate methods and may not
produce easily interpretable prediction rules.
  Results: We propose a new method for supervised classification of arrayCGH
data. The method is a variant of support vector machine (SVM) that incorporates
the biological specificities of DNA copy number variations along the genome as
prior knowledge. The resulting classifier is a sparse linear classifier based
on a limited number of regions automatically selected on the chromosomes,
leading to easy interpretation and identification of discriminative regions of
the genome. We test this method on three classification problems for bladder
and uveal cancer, involving both diagnosis and prognosis. We demonstrate that
the introduction of the new prior on the classifier leads not only to more
accurate predictions, but also to the identification of known and new regions
of interest in the genome.
  Availability: All data and algorithms are publicly available.
