My Research
As a result of my training at both the Netherlands Cancer Institute and the Delft University of Technology, I am a bioinformatics scientist with a solid background in computational data science and a strong desire to improve cancer genomics. My research focuses on creating cutting-edge machine-learning-inspired methods to increase the knowledge that can be retrieved from cancer patient omics data.
Cancer research increasingly relies on complex(big)data that capture multiple aspects of the same patient or sample. As a result, bioinformatics expertise becomes indispensable to i) provide data analytics methods that enable extracting relevant knowledge from the data, ii) create data integration methods to further our understanding of the complex interplay between biological variables and iii) facilitate FAIR data management to promote reproducibility and data sharing. My research group aims to address all three of these aspects.
Awards
- 2018: Oncode Clinical Proof of Concept study on applying CyclomicsSeq for Head and Neck Cancer (with Kloosterman)
- 2017: NIH-4D Nucleome TCPA (de Laat lab)
- NWO Veni (2012) and NWO Vidi (2017) recipient
Key Publications
- A Allahyar and C Vermeulen, ..., J de Ridder*, W de Laat*, Enhancer hubs and loop collisions identified from single-allele topologies. Nature Genetics, 2018. PMID: 29988121
- FJ Rang, WP Kloosterman*, J de Ridder*, From squiggle to basepair: computational approaches for improving nanopore sequencing read accuracy. Genome Biology, 2018. PMID: 30005597
- J Ubels, P Sonneveld, EH van Beers, A Broijl, MH van Vliet*, J de Ridder*, Predicting treatment benefit in multiple myeloma through simulation of alternative treatment effects. Nature Communications, 2018. PMID: 30054467
- S Babaei, W Akhtar, JJ de Jong, M Reinders, J de Ridder*, 3D hotspots of recurrent retroviral insertions reveal long-range interactions with cancer genes. Nature Communications, 2015. PMID: 25721899
- A Allahyar, J de Ridder*,FERAL: network-based classifier with application to breast cancer outcome prediction. Bioinformatics, 2015. PMID: 26072498
