My group is analyzing data from different proteomics technologies capable of characterizing the deep plasma proteome. The combination of data generated by data-independent mass spectrometry and affinity-based technologies enables us to annotate molecular signals and detect changes from a patient’s baseline.
This can be by integrating molecular and clinical data for translational research and diagnostic development, simplifying tailored patient treatment. The focus lies on liquid biopsies and how we may use this information to map the molecular landscape of health and disease. We can improve patient stratification by combining data from mass spectrometry and affinity-based approaches, which are often driven by longitudinal monitoring of an individual’s baseline. My research has focused on the dynamic proteome of individuals and how we might use blood indicators to develop improved prediction models. This data integration strategy and analysis can be utilized for patient stratification, diagnostic biomarker identification, and longitudinal monitoring and follow-up.