What we do

Our lab acts as the bridge between big data analysis and biomedical research. We develop novel data mining algorithms to detect patterns and statistical dependencies in large datasets from the fields of biology and medicine. Our major goals are twofold:

  1. to enable the automatic generation of new knowledge from big data through machine learning, and
  2. to gain an understanding of the relationship between biological systems and their molecular properties. Such an understanding is of fundamental importance for personalized medicine, which tailors medical treatment to the molecular properties of a person.

Find out more about the Group’s activities

Main publications 2021

  • O’Bray L et al.
    Filtration Curves for Graph Representation
    Proc. 27th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., 10.1145/3447548.3467442
  • Muzio G et al.
    Biological network analysis with deep learning
    Brief. Bioinformatics, 10.1093/bib/bbaa257
  • Gumpinger A C et al.
    Network-​guided search for genetic heterogeneity between gene pairs
    Bioinformatics, 10.1093/bioinformatics/btaa581

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