Life Science Data Analytics
and Algorithmic Bioinformatics

We are working 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 personalized medicine. In order to address both questions suitable data mining and 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 specifically concentrates on data analytical approaches for:

  • personalized / precision medicine: usage of multi-modal data, integration of biological background knowledge, integration of biological mechanisms.
  • molecular systems: algorithms for reconstruction, modeling and making predictions
  • complex phenotypes: e.g. algorithms for gait analysis