I am a Senior Data Scientist at DTU biosustain (Novo Nordisk Foundation Center for biosustainability) (NNF CBMR) in Copenhagen.

Nearly all of my work you can find currently under the github of DTU biosustain, the Multi-Omics-Analytics-Group (MONA) and the RasmussenLab.

Currently I'm working on metabolomics and phosphoproteomics dataset in the context of engineering cell factories. I also worked on 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 to checkout a comparison based on PIMMS on an Alzheimer dataset: rasmussenlab.org/pimms

For an easy comparison of proteomics data and some clincial metadata, have a look at not just another biomarker (njab). You can easily run the example notebook on colab (which is regularly automatically tested to work) and plug-in your data. Having the associated python package will it 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 wesite 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