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Our main research area is bioinformatics, especially computational
statistics applied to the analysis of "high-throughput" data, mainly in cancer.

Our work ranges from the application of standard techniques to the
development of new statistical approaches, with special emphasis in their implementation using high performance computing.

Some of the problems we have worked on include patient classification and gene differential expression using microarray expression data, survival analysis with "omics" data, functional annotation of results from analysis of "omics" experiments and, in the last years, mainly segmentation of array CGH data to detect copy number changes in genomic DNA and to identify recurrent regions of alteration in groups of patients. Our current work involves the use of phylogenetic methods and probabilistic graphical model to address problems of detecting recurrent regions of alteration in copy number and to discover tumor development paths and trajectories.