David Gomez-Cabrero
Associate Professor, Bioscience
Biological and Environmental Science and Engineering Division
Technical Platform Membership:
The Bioinformatics Platform
Building frameworks that help transform (vast) multimodal biomedical data into knowledge that improves how we understand and treat disease.
Program Affiliations
Research Interests
Professor Gomez-Cabrero is a bioinformatician and computational biologist. His research focuses on the development of system-based methodologies for understanding biological systems and its translational applications for understanding health and disease. Upon completing his Ph.D., he moved to Stockholm as postdoctoral research. Next, he was appointed Assistant Professor at Karolinska Institutet between 2014 and 2018. Next, he was appointed as Senior Lecturer at King's College London (2016-2018). During 2017 he became the head of the Translational Bioinformatics Unit at Navarrabiomed (Pamplona, Spain). His current goals are the developing of robust statistically-based methodologies and frameworks to uncover robust hypothesis in (possibly under-powered) research studies. Tools for multi-omic integration are being implemented and shared in the Bioconductor STATegRa package. Nowadays, the aim is to develop strategies for current integrative challenges such as "integrating bulk and single-cell data" and "integrating multi-omic single-cell data". Most of Professor Gomez-Cabrero's research is conducted in a translational context, especially working in Multiple Sclerosis, Rheumatoid Arthritis, rare diseases and Myelodysplastic syndromes, among others.
Computational Biology
Multi-omics data integration
Single-cell
Spatial Profiling
Bone Marrow
Education Profile
Postdoctoral Fellow, Karolinska Institutet, 2009-2014.
Ph.D. Mathematics, Universitat de València, 2005-2009.
Advances Ph.D. Courses in Operations Research and Statistics, Universitat de València, 2000-2003.
MSc Statistics, 2000-2004.MSc and BSc Mathematics, 1996-2000.
Publications
Research Areas
- Computational Bioscience
- Machine Learning