Banner image of three advanced courses

Programme overview

The Exposome Short Course Series is a multi-institution programme on Statistical models, Machine Learning Techniques and Bioinformatics tools to analyse Exposome data and by extension, OMICs data. It is run in Utrecht (Institute for Risk Assessment Sciences, Lead R Vermeulen), London (Imperial College, Lead M Chadeau-Hyam), and the University of Pau (Biarritz-Anglet Campus, Lead B Liquet). It comprises of three complimentary one-week courses focusing on (i) the introduction to OMICs data and their analyses, (ii) advanced modelling techniques for OMICs profiling and integration, and (iii) real-life application of these techniques for Exposome research and related areas.

Target audience 

The Exposome Short Course Series has been designed for scientists from academia and industry with interest in the analysis of high throughput data. The Exposome Basis course will be of interest to academics (students and researchers) and scientists from the industry (pharmaceutical companies, insurance companies, food industries etc.), and no prior knowledge is required, but experience in basic statistics, and use of statistical software (preferably R) is desirable.

Teaching format

The Exposome Short Course Series is dedicated to teaching state-of-the-art and cutting-edge statistical and machine learning techniques to a non-specialist audience. To achieve this goal the Exposome Basis and The Exposome Advanced courses are organised such that morning theoretical lectures are immediately illustrated by a seminar showing real-life application of these techniques, and a practical session where attendees will implement these techniques on real data. The Exposome Practice is focusing on the reproducible implementation of these techniques to address real research question from OMICs and Exposome research through a combination of supervised project work on real data sets and seminar and lectures.

General learning outcomes

At the end of the programme, attendees will be able to:

  • Define the concept of the Exposome and its complexity
  • Comprehend the main types of OMICs data and their specificities (Exposome Basis)
  • Implement and interpret results from the main OMICs profiling approaches (Exposome Basis)
  • Understand the challenges raised by OMICs integration and the ongoing methodological developments to perform these analyses (Exposome Advanced)
  • Design, implement and interpret models (including network models and machine learning techniques) to perform OMICs integration (Exposome Advanced)
  • Deploy these complementary techniques to address real exposome research questions (Exposome Practice)
  • Develop the ability to carry out research in a reproducible and translatable manner (Exposome Practice)

Who can apply?

This year we start the cycle of the programme with The Exposome Advanced, and The Exposome Practice courses, so that former students from the Imperial Stat-XP and the Utrecht Molecular Epidemiology Course (MEC), or any other scientist with a similar background can complete the training.

  • Advanced XP and Exposome Practice are open to former student of Stat-XP or MEC, or other students with basic experience in OMICs data analyses
  • It is recommended that students registering to Exposome practice also take the Exposome Advanced course.

Courses on offer

Exposome Basis (Utrecht University, NL)

2020 (exact date TBC)

The Molecular Epidemiology and Exposome Course (MEEC) is an introductory course providing a comprehensive introduction to the concept of the exposome and its practical implementation.

The course focuses on OMICs data, their features, and the challenges their statistical analysis raises. The MEEC proposes a series of lectures, seminars and practicals describing the main statistical methods used in molecular epidemiology. These include univariate models and multiple testing correction strategies (FWER, FDR), dimension reduction techniques, and variable selection approaches.

Exposome Advanced (Imperial College London, UK)

16-20 September 2019

XP Advanced is an advanced course presenting further techniques to analyse and integrate OMICs data in an exposome concept. The course builds upon a statistical background, such as the one taught in the MEEC course, to introduce necessary extensions of these methods in order to (i) accommodate complex study designs; (ii) Improve results interpretability;  and  (iii) handle multiple sets of OMICs data.   In addition to regression-based profiling approaches, XP Advanced also features the exploration of machine learning techniques, including network inference and their practical application to OMICs data. The course will develop the theoretical background of these methods and their applicability to OMICs data in Exposome Sciences.



Exposome Practice (ICL/University of Pau)

2020 (exact date TBC)

Surf 64 is a one-week summer school focusing on the practical application of methods and principles developed in the MEEC and XP Advanced courses to real data.

The summer school will consist of five days of supervised group work using real data sets and addresses real research questions on OMICs analysis, interpretation and integration.

The course also includes a series of lectures, seminars, and tutorials illustrating solutions to OMICs profiling and integration in a real-life setting using regression-based approaches, machine learning techniques and network topologies.

Courses on offer

Course Directors:
Prof R Vermeulen, Professor of Environmental Epidemiology and Exposome Science, University of Utrecht
Dr J Vlaanderen, Assistant Professor, University of Utrecht



Course Director:
Dr M Chadeau-Hyam, Reader in Computational Epidemiology & Exposome Science, Imperial College London


Early bird (book before July 15, 2019):
Academic: £1,000
Non-Academic: £1,300

Standard registration:
Academic: £1,200
Non-Academic: £1,500

Course Directors:
Dr M Chadeau-Hyam, Reader in Computational Epidemiology & Exposome Science, Imperial College London
Prof B Liquet, Professor of Statistics, University of Pau Pays de l'Adour, Anglet, Affiliated to ACEMS, Queensland University Technology