Summary
Metabolomic data integration
BATMAN NMR modelling
Metabolomics power analysis
Differential association networks
Large scale metabolomic data processing
Short time series analysis
Data visualisation
Metabolomic/transcriptomic pathway analysis
Modelling mass spectrometry data
I am a Reader within the Computational and Systems Medicine Section of the Department of Surgery and Cancer. My overall research interests lie at the interface between two broad areas:
multivariate data analysis,
and
post-genomic technologies.
More specifically, on the computational side these include, machine learning, bioinformatics, chemometrics, and multivariate statistics, and on the experimental side, the fields of genomics, transcriptomics and metabolomics. I am interested in applying diverse computational and mathematical methods in order to disentangle the mass of information at multiple biological levels generated by the –omics technologies. The ultimate aim is to synthesise the information provided by each of these techniques, thus facilitating a multi-scale understanding of biological systems. This broad aim leads to several themes in my current research:
- Improving information extraction from Nuclear Magnetic Resonance (NMR) spectroscopy & Liquid Chromatography–Mass Spectrometry (LC-MS) metabolic profiles
- Novel methods for predictive modelling of post-genomic data
- Statistical association networks as complex phenotypes in post-genomics
- Statistical integration and visualisation of metabolic profiles with other post-genomic data
- Time series analysis of post-genomic data
Current projects are detailed on my Research page.
teaching
Dr Tim Ebbels introduces the Biomedical Research stream of the MRes in Biomedical Research
Selected Publications
Journal Articles
Tzoulaki I, Castagné R, Boulangé CL, et al., 2019, Serum metabolic signatures of coronary and carotid atherosclerosis and subsequent cardiovascular disease, European Heart Journal, Vol:40, ISSN:1522-9645, Pages:2883-2896