Computational Biology & Bioinformatics in Immunology and Cancer

Department                            Immunology

Principal investigator          Harmen van de Werken

E-mail address            


Various projects related to computational biology & bioinformatics in immunology and cancer

The CBBI group’s scientific endeavors are firmly rooted in four fundamental pillars:

Integrating Big Data with Clinical Data: Leveraging advanced statistical learning methods, including Machine and Deep Learning, to enhance clinical decision-making. Our innovative approaches have allowed us to accurately predict the severity of COVID infections and forecast the metastatic spread of solid tumors, such as skin cancer.

Exploring Disease Onset and Development: Using Big Genomic Data to gain insights into the initiation and progression of various diseases, uncovering new avenues for scientific exploration. We’ve successfully unraveled genomic alterations in prostate, bladder, and neuroendocrine cancers, as well as examined gene expression changes in the immune response of Sjögren Syndrome. Additionally, we’ve delved into single-cell genomic analysis in Hepatitis B patients.

Developing Cutting-Edge Algorithms: We frequently design novel algorithms to extract critical biological information from Big Genomic and Cellular Data. Our algorithms can identify clustered mutations on the genome and even predict genome folding in three dimensions.

Empowering Scientific Data Processing: We develop tools and implement algorithms for high-end scientific data processing, enabling the visualization of extensive genomics datasets.

Our student projects are seamlessly integrated into ongoing research initiatives, drawing from one or more of the aforementioned CBBI pillars. For instance, we are currently engaged in a project to enhance the prognosis of skin carcinoma metastasis by harnessing genomics, imaging, and clinical data through machine learning techniques. We are also dedicated to deepening our understanding of immune diseases in collaboration with 12 countries within the EU ImmunAID consortium. Furthermore, we have access to extensive Whole Genome and Transcriptome datasets from cancer patients through the Hartwig Medical Foundation. These datasets allow us to investigate drug resistance causes, as well as interactions between the immune system and cancer. Moreover, we have numerous other projects related to the immune system, cancer, and Big Data.


  • Computational algorithms
  • Artificial intelligence
  • Machine learning techniques
  • Visualization tools

Further reading

Van de Werken, H.J.G. et al. (2012). Nature Methods, 9, 969–972. DOI: 10.1038/nmeth.2173.

Van de Geer, W.S., et al. (2022). Bioinformatics, 38, 1437–1439. DOI: 10.1093/bioinformatics/btab809.