Bioinformatics in molecular medicine
diagnostics, sequencing, nanopore, machine learning
Research aim
We are a bioinformatics lab that creates and applies innovative data science methods to advance our understanding of disease biology and molecular medicine with a focus on cancer research.
About us
Machine learned diagnostics with impact
We specialize in developing advanced machine learning and artificial intelligence algorithms to achieve various critical (pre-)clinical objectives using modern omics data. For instance, our recently published Sturgeon algorithm (Nature, Oct 2023) we drastically reduced the time required for (pediatric) CNS cancer diagnostics with native DNA Nanopore sequencing and methylation classification using deep learning. This enables truly intraoperative diagnosis, directly influencing the treatment. This is a clear example of our constant quest for actual, clinical, bedside impact. To facilitate these results, we also focus on laying the necessary groundwork, such as by rigorously comparing the newest Nanopore base-calling models and studying colorectal cancer cell evolution at a single-cell level. Interest areas in the lab are broad and range from GWAS, to liquid biopsy classification under data sparsity, to 3D genome conformation and epigenetics. Additionally, we create classification models for patient cohort and clinical trial data to improve diagnostics and enable personalization of treatment strategies. Our track record includes the development of classifiers which can predict treatment benefit in multiple myeloma and colorectal cancer.
Our lab is mainly funded by the Oncode Institute, NWO Vidi grant and ERC consolidator grant, among others.