I am a Senior Data Scientist at DTU Biosustain (Novo Nordisk Foundation Center for Biosustainability) in Copenhagen.
You can find nearly all of my work on GitHub under the organisations DTU Biosustain, the Multi-Omics-Analytics-Group (MONA), and RasmussenLab.
Currently, I'm working on metabolomics and phosphoproteomics datasets in the context of engineering cell factories. I also worked on self-supervised deep learning models for MS-based proteomics imputation. See the PIMMS repository and the Nature Communications paper. You might also be interested in checking out a comparison based on PIMMS on an Alzheimer's dataset: rasmussenlab.github.io/pimms/
For an easy comparison of proteomics data and some clinical metadata, have a look at not just another biomarker (njab). You can easily run the example notebook on Colab (which is regularly and automatically tested to work) and plug in your data. Having the associated Python package will make it easy to extend the experiments. The code was used in a Nature Medicine paper and a Scientific Reports paper.
- An easy-to-use notes template
- Lab website for technical notes (based on Sphinx, deployed via GitHub)
- ProteoBench - focus on software parameter file parsing
ProteoBench is an open and collaborative platform for community-curated benchmarks for proteomics data analysis pipelines. Our goal is to allow a continuous, easy, and controlled comparison of proteomics data analysis workflows.
- Creating and curating a Python package template