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We are interested in developing new approaches for the integration 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 & 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:

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.

Projects and Networks

We are part of different international projects and networks, including:

TransBioNet, the Translational Bioinformatics Network coordinated by the Spanish National Bioinformatics Institute (INB) 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.

– The PRECISESADS project aims to use multi-omics data to establish a classification of systemic autoimmune diseases based on molecular patterns. We are focused on the implementation of state-of-the-art clustering and machine learning methods to establish new disease classification schemas  (Toro-Domínguez et al. 2018).

– The ONCONET-SUDOE, a cooperation network in oncology, co-financed by the European Interreg Sudoe Programme. In collaboration with Parque Tecnológico de Ciencias de la Salud, we have promoted initiatives such as the Oncothon, a datathon-oriented event to explore the potential of cancer genomics data to open new pathways for cancer diagnosis and treatment.