About
"From identifying proteomics biomarkers for disease to leading multi-omics biomarker platforms — the question hasn't changed, only the scale has."
Career Journey
From Bench to Bytes
My scientific journey began in proteomics, when I gained my first level of research experience with my master thesis in the area of proteomics and mass spectrometry. This led me to pursue PhD-level research in mass spectrometry-based proteomics — working at the intersection of analytical chemistry and computational biology to identify biomarkers, decode protein function and protein-protein interaction networks.
Over the years I worked across the full breadth of proteomics — from disease biomarker discovery in serum and plasma, to cellular proteomics, protein-protein interaction studies and PTM-specific platforms spanning phosphorylation, glycosylation and cysteine modifications. I also designed and taught practical proteomics data analysis courses, translating complex computational workflows into accessible training for the next generation of researchers.
Today, I channel that foundation into AI-driven drug discovery — leading the development of biomarker modules for omics research, building data pipelines and integrating large language models to transform raw biological data into actionable insights for pharmaceutical innovation.
Deep Expertise
What I Actually Know
- LC-MS/MS experimental design & data acquisition
- Database searching (MaxQuant, Mascot, Proteome Discoverer)
- Label-free and TMTquantification
- Post-translational modification (PTM) analysis
- Protein complex & interaction network mapping
- LLM prompt engineering & pipeline integration
- ML model application for biological data
- AI-assisted biomarker candidate scoring
- Data curation and ETL for omics workflows
- Python & R for ML pipelines
- Evaluation frameworks for model outputs in science
- Multi-omics data integration
- Statistical modelling for high-dimensional biological data
- Differential expression & enrichment analysis
- Data visualisation (ggplot2, ComplexHeatmap)
- Reproducible pipeline development in R & Python
- Biomarker module architecture & product management
- Cross-functional team leadership (science × engineering)
- Scientific communication for non-technical stakeholders
- Agile delivery in research-product environments
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