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:
- Approaches to integrate and analyze -omics datasets from different origins (Carmona-Saez et al., 2017, Toro-Domínguez et al. 2014).
- Integrative analysis of methylome and transcriptome data (Martorell et al., mCSEA. 2019).
- Statistical Methods and computational tools for meta-analysis of -omics data (MetaGENyO and ImaGEO).
- Drug Repurposing and statistical methods for comparing Gene Expression Signatures (Vazquez et al., 2010, Toro-Domínguez et al., 2017).
- Functional and Network analysis (GENECODIS)
Dissecting the Molecular Basis of Complex Diseases
- Biomarker Discovery from omics data (Rodríguez-Martínez et al. 2019, Melero et al. 2016, Ramos et al. 2019)
- Patient stratification and disease classification (Toro-Domínguez et al, 2018)
- Drug discovery and Pharmacogenomics (Toro-Domínguez et al. 2017, Díaz et al. 2016)
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.