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We are interested in developing new approaches for the integration of omics data with applications for biomarker discover, 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.

Multi-Omics Data Integration

One of our aims is to develop new computational and statistical methods for the integration and analysis of heterogeneous omics data. In this context, we closely collaborate with experimental groups to develop methods to exploit large-scale biological datasets in a broad range of contexts:

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 that aims to use multi-omics data to establish a classification of systemic autoimmune diseases based on molecular patterns rather than clinical manifestations. Our efforts here are focused on the implementation of state-of-the-art clustering and machine learning methods that help us to integrate and establish new classification schemas for these diseases (Toro-Domínguez et al. 2018).

– The ONCONET-SUDOE, a cooperation network in oncology, co-financed by the Interreg Sudoe Programme through the European Regional Development Fund.  In collaboration with Fundación Parque Tecnológico de Ciencias de la Salud de Granada – PTS, we have promoted initiatives such as the Oncothon, a datathon-oriented event that brought the opportunity to bioinformaticans and biomedical/clinical researchers to work together and explore the potential of cancer genomics data to open new pathways for cancer diagnosis and treatment.