Overview
Research in our group aims at contributing to narrow the gap between sequence
and function by developing computational tools for a better understanding of
biological mechanisms in the context of knowledge of whole genome structure.
More concretely, our research lines are:
- Reverse-engineering of molecular regulatory networks
- Identification of functional binding sites
- Evolution of molecular regulatory networks
- Identification of transcriptional dysregulatory programs in cancer
- Limited-order graphical Markov models
Ramon y Cajal fellow, ref. RYC-2006-000932
Machine learning of graphical Markov models through qp-graphs: application to molecular regulatory network modeling, ref. TIN2008-00556/TIN
COMBIOMED network, ref. RD07/0067/0001
