We are interested in developing new approaches for integrating of omics data with applications for biomarker discovery, patient stratification and classification, among others. Some of these methods are available as public bioinformatics tools that can be used by researchers to study and interpret their data.
Statistical and ML methods for Multi-Omics Data Integration
One of our aims is to develop new statistical, computational and machine learning methods for the integration and analysis of heterogeneous omics data in a broad range of contexts:
- Multi-omics data integration
- Methods for meta-analysis of biomedical data
- Pathway analysis and Network analysis
Dissecting the Molecular Basis of Complex Diseases
We closely collaborate with experimental groups to analyze and integrate large-scale biological datasets in order to get a better understanding of the molecular mechanisms of complex diseases.
- Biomarker Discovery
- Patient stratification and disease classification
- Drug discovery and Pharmacogenomics
Our group participates in international and national networks and research consortiums
TRANSBIONET, the Translational Bioinformatics Network coordinated by the Spanish National Bioinformatics Institute (INB) that has been created as the reference network for Translational Bioinformatics that brings together most of the bioinformatics units and groups working at health care settings.
Financial support from
