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How can we extract more knowledge from mass spectrometry data?

Mass spectrometry is a widely used and very powerful analytical technique to identify proteins and small molecules in complex biological samples. Unfortunately, in most cases only a minority of the experimental "spectra", the data generated by the mass spectrometer, can be successfully identified. We develop and study advanced bioinformatics and machine learning techniques to derive novel knowledge from this untapped source of valuable data, for example to improve the identification of hitherto unseen proteins and metabolites. A complementary focus area is a smart data-driven approach to quality control for biological mass spectrometry to assess the reliability of the experimental results.

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