Our Mission: Bringing Better Treatments to the Right Patients
The research of our team has a methodological as well as an application oriented component, where method development is typically driven by specific questions arising in applications (for instance in pharma industry). Currently these applications largely cover:
- precision medicine (the right drug for the right patient):
- AI based modeling of disease risk, disease progression and disease subtypes
- AI based simulation of synthetic patient data as a mechanism for privacy preserving data sharing
- early drug discovery (better drug targets):
- AI based drug target prioritization
- AI methods for adverse event prediction
In addition, we have a long standing experience with applications of AI in system medicine (reverse engineering and simulation of biological networks).
To address the highly complex questions arising in our different applications a broad range of different AI and data science techniques is needed (covering neural networks, Bayesian learning, Bayesian Networks, kernel methods, boosting and others). At the same time, off-the-shelf solutions rarely provide satisfactory results. Hence, a significant proportion of our work goes into the adaptation, development and design of AI and data science techniques that are tailored to solve a particular application problem. During the last years our method developments have specifically covered
- »Hybrid« AI: combination and integration of knowledge (e.g. in form of graphs) into machine learning models
- (Generative) modeling of multivariate time series, including approaches to deal with missing values
- Models that deal with multiple data modalities and biological scales.
We have historically a long standing experience with various types of -omics data, but during the last years other data types (e.g. clinical, real world evidence, bio-imaging derived features) have become more and more important.