Research Research departments Center for Molecular and Biomolecular Informatics

Center for Molecular and Biomolecular Informatics

The Center for Molecular and Biomolecular Informatics (CMBI) does research and education, and provides services in bioinformatics and cheminformatics.

Merging of departments

As of 1 February 2023, this department is part of the new research department Medical BioSciences, headed by Jolanda de Vries. More information will follow shortly.

Contact Head of the CMBI

prof. dr. Jolanda de Vries
head of department

contact form

Mission & vision

Our mission

Our mission is to add value to personal health data by their translation into integrative knowledge and actionable information.

Our vision 

  • CMBI develops bioinformatics approaches that contribute to the understanding of disease mechanisms, personalized therapies and interventions, and a learning health care system
  • CMBI is committed to the reusability of their data, tools, and services
  • CMBI provides  bioinformatics researchers in Radboudumc with a platform for exchange of knowledge and expertise
  • CMBI contributes to the education of BMW, MLW, and MMD students so that they can apply and understand the principles behind (existing) bioinformatics tools

Radboudumc Technology Center Bioinformatics

The Bioinformatics technology center aims to raise and solve biological questions using the most recent computer technologies and big data solutions.

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Our mission is to provide a basic understanding in Bioinformatic principles. Follow-up courses are available for those who want to gain greater insight in our field. read more


Researchers at the CMBI contribute to several courses at both the Faculty of Science and the Medical Faculty. We provide courses in Structural Bioinformatics, Comparative Genomics, Data Analysis, and programming courses such as Java. We specifically focus on the Molecular Life Science students, Biomedical Sciences students and participants in the master Molecular Mechanisms of Disease, although some of our courses can be chosen by Biology, Chemistry or Medical students as well. 

Our mission is to provide a basic understanding in Bioinformatic principles for bachelor students as this was shown to be beneficial for those who want to pursue a career in Life Sciences. Follow-up courses are available for those who want to gain greater insight in our field. 

For MLS/Biology students it is even possible to follow the B-track by choosing a combination of (master) Bioinformatics courses and internships at Bioinformatics departments. 

Our researchers also teach in special interest courses and summer schools here at the Radboudumc, the Radboud University and elsewhere.


For more information, you can contact dr. Hanka Venselaar, education coordinator at the CMBI.




The CMBI offers a wide range of possibilities for internships. read more


Both bachelor and master students from studies such as Molecular Life Sciences, Biomedical Sciences, Chemistry and MMD are welcome. In general, we are flexible in terms of internship length, type of internship and type of research. 

Below you can find our internship projects:

  • Supervisors:

    Peter-Bram ‘t Hoen (Bioinformatics, Radboud university medical center), Jelle Piepenbrock, Tom Heskes (Computer Science, Radboud university)


    There are about 6,000 different rare, genetic disorders. For the majority of patients living with these disorders, there are currently no treatment options. Developing drugs for each of these disorders will be a tedious and costly process. Drug repurposing, the reuse of approved drugs for new indications, is an interesting treatment strategy. The clinical development trajectory is usually much shorter than for development of entirely new drugs, and the expenses for their development are lower. Deep learning may be used to predict drug repurposing candidates, because it is able to reason over the existing knowledge about drugs, their current indications and their targets.

    We have set up a deep learning framework involving graph neural networks (GNNs) for the prediction of new drug repurposing candidates. GNNs are particularly suitable for this task because they can exploit a knowledge graph that contains all the known relationships between genes, drugs and diseases. Prediction of new drug repurposing candidates can be defined as a link prediction task, predicting the likelihood of new associations between an existing drug and a given rare disease. 

    When setting up the GNN framework, we encountered several issues, which we would like to tackle in the current project. (1) the need for a validation strategy. We propose to exploit knowledge graphs that are based on older versions of databases to predict the drugs approved for clinical indications in recent years; (2) the need for providing the underlying evidence for newly predicted associations. We suggest to use explainable AI techniques such as GNNExplainer that present subgraphs of nodes and edges that contribute most to the prediction to the end user; (3) the importance for weighting the different sources of information. Several strategies can be explored here.   


    • Studying for a Master degree in Bioinformatics, Computer Sciences, Artificial Intelligence, Molecular Life Sciences or similar - Student should be available for at least 5 months
    • Basic python programming experience is essential
    • Experience in deep learning is an advantage


    • An internship in a multidisciplinary setting with supervisors with complementary expertise
    • Experience of being embedded in a research group with related activities such as journal clubs and social events
    • Weekly and ad-hoc meetings with supervisors
    • Ample opportunities to present your work in internal and external meetings

    Related literature:

    1. Ioannidis et al. arXiv (2020)
    2. Wang et al. Briefings in Bioinformatics (2021)  
    3. Ying et al. NeurIPS (2019)


Bioinformatics Services

We maintain computational facilities, databases, and software packages in bioinformatics. read more

Diagnostics-in-3D progress update 2021

Update on the EFRO funded project Diagnostics-in-3D. read more

Diagnostics-in-3D progress update 2021

About 7,000 rare hereditary diseases affect ±8% of the EU population, which translates to ±36 million people. Identification of causative mutations of such diseases thus, forms an essential step towards diagnosis as well as towards development of treatment. At CMBI, along with a multidisciplinary team of experts from BioProdict and Vartion, we are making efforts to predict (and eventually explain) functional effects of variants of unknown clinical significance.

Our EFRO funded project Diagnostics-in-3D uses an advanced deep learning framework known as DeepRank where we leverage information on protein structural features surrounding missense variants, coupled with the evolutionary significance of variant positions, and allow neural networks to learn from such variant environments. This exercise produces a probability estimate classifying whether a variant is disease-causing or not.

We have tailored DeepRank's 3D-CNN framework to help address our problem statement. One of the key aspects of this project is the diversity in variant environments captured from protein structures that differ depending on various protein families they belong to. Taking this aspect into account, we are now in the process of performance evaluation of our tool.


Our researchers CMBI

A list of researchers connected to this department. read more

Getting there


Go to Floor 0 and follow route 260

Contact Business manager

Arthur Willemsen
Business manager


Contact Operational manager

Barbara van Kampen
Operational manager

+31 (0)24 361 93 90

Contact Management assistent

Sandra de Leeuw
Management assistent


Research themes