Multi-Omics and Data Sciences in Complex Diseases

Many diseases are complex, their polygenic nature combined with gene-environment interactions leads to a situation where single omics are insufficient to both understand the biology and to make reliable diagnostic or prognostic predictions. This course will cover advanced multi-omics screening technologies and look at how we use cutting edge data science to make sense of our measurements and bring explainability to our predictions, which will ultimately lead to hypothesis generation and furthering biological understanding.  


Course coordinators

  • Dr. Lars Eijssen (Maastricht University, FHML)
  • Dr. Rachel Cavill (Maastricht University, FSE)


Course credits

1.5 ECTS for following the course, 2 ECTS when successfully completing a final assignment.


Course overview

The ‘Multi-omics and data sciences in complex diseases’ course will focus on the application of modern high-throughput screening technologies, in combination with advanced integrative data sciences approaches, to unravel the complexity of multifactorial diseases.

The start of the week will focus on epigenetics and the studying of interactions between genes and the environment, followed by a discussion of the various epigenomics methodologies and analysis workflows.  From there we move to an exploration of omics data integration methods and ways to obtain explainability from our black box machine learning models, as well as knowledge-driven integration approaches.  We finish off the week with some general considerations related to advances in the field and opportunities for you to ask all your remaining questions or discuss your data with our experts.

The course will contain a mixture of lectures, interactive sessions and hands-on computer practicals, to make sure that you have a chance to grasp both the theory and the practical implementation of all that we present. As participants, you will have ample opportunity to interact with each other as well as with faculty members.

NB: It is required to bring a laptop to the course, further instructions on software to install in preparation for the course will be provided to all participants prior to the course starting. The hands-on sessions will include computer scripting tasks and workflows, therefore it is recommended that you have some experience of working with scripting languages (such as R or Python), alongside experience of single omics data analysis pipelines.


Course program

  • Epigenetics and gene-environment interactions
  • Epigenomics screening technologies and analysis workflows
  • Molecular epidemiology to understand environmental contributions
  • Analysis of gene-gene and gene-environment interactions
  • Machine learning and multi-omics integration methods
  • Interpretation and explainability of machine learning methods and their results
  • Network-based and knowledge-driven integrative analysis methods
  • General considerations related to big data in multi-omics for complex diseases
  • Opportunity to ask your own dedicated questions and expert advice



Senior Faculty, domain experts for the various topics; instructors are mostly based at Maastricht University, Faculty of Health, Medicine and Life Sciences (FHML), and Faculty of Sciences and Engineering (FSE), who jointly host this course.


Target audience

Our target audience is people who have experience with basic single omics workflows and want to learn to extend these skills into multi-omics analyses and examine in more detail how to use these datasets to understand complex diseases or other complex phenotypes. People of all levels, including PhD candidates, postdoctoral fellows and PIs, are welcome to participate in this course.


Learning outcomes

At the end of the course you will be able to:

  • design studies to investigate genetic and environmental contributions to complex disease;
  • comprehend current approaches – technologies and workflows – used for epigenomics screens;
  • appreciate how epigenomics studies can help unravel gene-environment interactions;
  • understand commonly applied statistical approaches to data analysis in omics analysis for complex disease;
  • understand how to apply machine learning methods to integrative multi-omics analysis and how they can be made explainable;
  • understand how to apply current bioinformatics approaches for integrative multi-omics analysis;
  • biologically interpret results from data-driven and knowledge-driven approaches;
  • appreciate big data-related aspects of multi-omics studies in complex disease.

For more information, contact Lars Eijssen or Rachel Cavill.



Early bird registration (until July 29 , 2024)

  • 400 (excl. VAT) for PhD/MSc students
  • 600 (excl. VAT) for academic researchers (non-profit)
  • 900 (excl. VAT) industry participants (for profit)

From July 30, 2024 onwards:

  • 480 (excl. VAT) for PhD/MSc students
  • 720 (excl. VAT) for academic researchers (non-profit)
  • 1080 (excl. VAT) industry participants (for profit)

The course fee includes course materials and catering (coffee, tea and lunch)

You can register for the course by filling out this registration form.



If you would like to join this course using a wildcard (more information on this topic soon on this website), please contact the community manager, Petra Aarnoutse.
Until the maximum number of wildcard spots is reached, you can register with a wildcard until August 12, 2024.
Always mention in your application from which academic group the wildcard comes. Your registration is only valid after confirmation from the community manager.


Find general enrollment information here.