Oriol Canela-Xandri Research Group
High performance algorithms to analyse large and distributed datasets
Section: Disease Mechanisms / Biomedical Genomics
Research in a Nutshell
Being able to extract useful information from genetic and health data is a key challenge for the next decade.
Human genomic data is doubling in size every seven months and may soon exceed other Big Data generators such as astronomy, YouTube and Twitter. Although this data is revolutionising medicine and development of new therapeutics and diagnostics, reaching the full potential of this substantial investment relies on our ability to unlock clinically actionable knowledge from it. The key to this is ensuring data access and analysis capabilities are democratised over a range of researchers and institutions with different capabilities, whilst ensuring individual level data is kept safe. However, obstacles to this remain, owing to the data's unprecedented size, complexity, and restricted access (relating to privacy concerns, economic interests, research limitations, and multi-institutional data fragmentation).
Our group investigates new mathematical and computational approaches to address these challenges. We also apply these methods to analyse existing data to try to understand the underlying genetic mechanisms under human complex traits.
|Oriol Canela-Xandri||Chancellor’s Fellow|
Partners and Funders
- Innovate UK
- University of Edinburgh
High Performance Computing, Genetics, Statistics, Complex Traits
High Performance Computing, Statistics, Methods Development, Programming and Markup Languages (C/C++, Python, JS, HTML/CSS, SQL, Assembler)