Biomedical Data Science
(formerly: Algorithmic Bioinformatics)
Research focus

The work of our group falls within the intersection of data sciences / data analytics and life sciences. Big data plays an increasing role in modern biomedical and biopharmaceutical research. Examples include -omics data (genomics, transcriptomics, methylomics, proteomics, ...) reflecting biological phenomena at the intracellular level, data from clinical and electronic medical records (EMR), bioimages, data from mobile devices and video recordings. A key questions is, how to utilize these data in order to generate value for patients, for example by better understanding of disease mechanisms and stratification of patients according to their expected treatment success, disease prognosis and early disease onset. The latter approach is also called precision medicine. In order to address both questions suitable data mining and Artificial Intelligence (AI, specifically machine learning) algorithms are crucial and need to be developed and applied. In that context one has to take the specific aspects of the employed data (e.g. high noise level, high dimensionality, large heterogeneity) into account. Furthermore, a critical factor for success and acceptance by medical and life scientists is typically the appropriate consideration and integration of background domain knowledge.

The focus of our research lies on development and application of algorithms for the questions outlined above. Historically, a major focus has been on -omics data, but also other data types (e.g. bioimages, health claims records) occasionally play a role and are of interest.

The current research - since 09/2015 mostly done at UCB - specifically concentrates on approaches for:

  • precision medicine:
    • AI based modeling of disease risk, disease progression and disease subtypes
    • enhancing AI models with existing knowledge about disease mechanism 
    • AI based simulation of virtual patient cohorts
    • multi-modal data fusion
  • systems medicine
    • AI methods for learning and adapting mechanistic models to data
  • early drug discovery:
    • AI based target prioritization
    • AI methods for adverse event prediction

Starting from 12/2019 the group will be part of Fraunhofer SCAI.