Department Center for Molecular and Biomolecular Informatics

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

Education

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.

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.