  We propose a methodology for the identification of transcription factors
involved in the deregulation of genes in tumoral cells. This strategy is based
on the inference of a reference gene regulatory network that connects
transcription factors to their downstream targets using gene expression data.
The behavior of genes in tumor samples is then carefully compared to this
network of reference to detect deregulated target genes. A linear model is
finally used to measure the ability of each transcription factor to explain
those deregulations. We assess the performance of our method by numerical
experiments on a breast cancer data set. We show that the information about
deregulation is complementary to the expression data as the combination of the
two improves the supervised classification performance of samples into cancer
subtypes.
