Imperial College London


Faculty of MedicineDepartment of Surgery & Cancer

Reader in Computational Bioinformatics



+44 (0)20 7594 3160t.ebbels Website




131Sir Alexander Fleming BuildingSouth Kensington Campus





  • Metabolomic data integration

    Metabolomic data integration

  • BATMAN NMR modelling

    BATMAN NMR modelling

  • Metabolomics power analysis

    Metabolomics power analysis

  • Differential association networks

    Differential association networks

  • Large scale metabolomic data processing

    Large scale metabolomic data processing

  • Short time series analysis

    Short time series analysis

  • Data visualisation

    Data visualisation

  • Metabolomic/transcriptomic pathway analysis

    Metabolomic/transcriptomic pathway analysis

  • Modelling mass spectrometry data

    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,


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:

  1. Improving information extraction from Nuclear Magnetic Resonance (NMR) spectroscopy & Liquid Chromatography–Mass Spectrometry (LC-MS) metabolic profiles
  2. Novel methods for predictive modelling of post-genomic data
  3. Statistical association networks as complex phenotypes in post-genomics
  4. Statistical integration and visualisation of metabolic profiles with other post-genomic data
  5. Time series analysis of post-genomic data

Current projects are detailed on my Research page.  


Dr Tim Ebbels introduces the Biomedical Research stream of the MRes in Biomedical Research

medicine, postgraduate study, biomedical, teaching



Ye L, De Iorio M, Ebbels TMD, 2018, Bayesian estimation of the number of protonation sites for urinary metabolites from NMR spectroscopic data, Metabolomics, Vol:14, ISSN:1573-3882

Posma JM, Garcia-Perez I, Ebbels TMD, et al., 2018, Optimized Phenotypic Biomarker Discovery and Confounder Elimination via Covariate-Adjusted Projection to Latent Structures from Metabolic Spectroscopy Data, Journal of Proteome Research, Vol:17, ISSN:1535-3893, Pages:1586-1595

Kaluarachchi M, Boulange CL, Karaman I, et al., 2018, A comparison of human serum and plasma metabolites using untargeted H-1 NMR spectroscopy and UPLC-MS, Metabolomics, Vol:14, ISSN:1573-3882

Harada S, Hirayama A, Chan Q, et al., 2018, Reliability of plasma polar metabolite concentrations in a large-scale cohort study using capillary electrophoresis-mass spectrometry, Plos One, Vol:13, ISSN:1932-6203

Schober D, Jacob D, Wilson M, et al., 2018, nmrML: A Community Supported Open Data Standard for the Description, Storage, and Exchange of NMR Data, Analytical Chemistry, Vol:90, ISSN:0003-2700, Pages:649-656

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