AI & Data Science

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 the pharma industry). Currently, these applications essentially cover

  • better drug targets:
    • AI methods for drug target prioritization
    • AI methods for adverse event prediction
  • precision medicine – the right drug for the right patient:
    • AI methods for prediction of disease risk, disease subtypes/strata, disease progression, treatment response
  • better trials:
    • enrichment trials
    • synthetic controls
    • digital twins

To address the highly complex issues that emerge in our different applications, a broad range of AI and data science techniques is needed (covering various types of neural network architectures, 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/knowledge infusion: Methods that infuse human knowledge (knowledge graphs or differential equations) into data-driven machine learning models
  • (generative) modeling of multivariate time series, including approaches to deal with missing values
  • models that deal with multiple data modalities across biological scales.

We have a long-standing experience with different types of data:

  • -omics
  • longitudinal clinical studies
  • various types of real-world data:
    • structured and unstructured electronic health record (EHR) and claims data
    • digital “biomarkers”: data coming from digital device technologies, e.g., gait sensors, smartphone applications

Our Service: Solving Problems for our Customers

Our services cover the entire value chain in translational biomedical research in the biotech and pharmaceutical industry. We offer companies tailor-made solutions in AI and data mining, for example through contract research. This way we are able to address different needs of our customers and to add temporary resources and know-how to their internal projects.

Precision medicine, Artificial Intellligence, Cancer, Biomarkers, Systems biology, Statistics, Neurology, Genetics, Network biology, Epidemiology, Mathematical modeling, Signal processing, Computational biology, Graph theory, Chemoinformatics, Signal processing, Data semantics

Selected Publications

  • de Jong, J., Emon, M. A., Wu, P., Karki, R., Sood, M., Godard, P., ... & Fröhlich, H. (2019). Deep learning for clustering of multivariate clinical patient trajectories with missing values. Giga Science, 8(11), giz134.
  • Khanna, S., Domingo-Fernández, D., Iyappan, A., Emon, M. A., Hofmann-Apitius, M., & Fröhlich, H. (2018). Using multi-scale genetic, Neuoimaging and clinical data for predicting Alzheimer’s disease and reconstruction of relevant biological mechanisms. Scientific reports, 8(1),11173.
  • Philipp Wendland, Colin Birkenbihl, Marc Gomez-Freixa, Meemansa Sood, Maik Kschischo, Holger Fröhlich (2022). Generation of realistic synthetic data using Multimodal Neural Ordinary Differential Equations. npj Digital Medicine volume 5, Article number: 122.
  • Johann de Jong, Ioana Cutcutache, Matthew Page, Sami Elmoufti, Cynthia Dilley, Holger Fröhlich, Martin Armstrong (2021). Towards realizing the vision of precision medicine: AI based prediction of clinical drug response. Brain, Volume 144, Issue 6.
  • Birkenbihl, C., Salimi, Y., Fröhlich, H. (2021). Unraveling the heterogeneity in Alzheimer's disease progression across multiple cohorts and the implications for data-driven disease modeling. Alzheimer's & Dementia 2022; 18: 251– 261.

Overview about our research projects

    Development of (generative) AI models for the prediction and simulation of therapy response and therapy side effects
  • PREDICTOM (Innovative Health Initiative / EU)
    AI-screening-platform for identifying dementia risk
  • Real4Reg (HORIZON Europe)
    AI models for real-world data
  • PsychSTRATA (HORIZON Europe)
    AI/ML models for precision psychiatry
  • CePPH
    AI/ML models for precision medicine
  • ParKInsonPredict (BMBF)
    AI for modeling of Parkinson's Disease progression
  • ADIS (EU Joint Programme Neurodegenerative Disease Research)
    Coordinator, AI methods for biological system modeling and disease diagnosis
    AI based models for early detection and monitoring of pandemic situations as well as decision support
  • DIGIPD (European Partnership on Personalised Medicine)
    Coordinator, AI methods for patient stratification
  • NFDI4Health (DFG)
    AI methods for synthetic patient data

    AI-screening-platform for identifying dementia risk
    AI based risk models using real-world data
  • Scientific Machine Learning
    Development of hybrid machine learning approaches, which combine mechanistic differential equation models with neural networks.
  • Graph Machine Learning
    Development of a modern Graph Neural Network approach to support drug target selection.
  • Modeling of Disease Trajectories
    Development and application of unsupervised machine learning approaches to cluster longitudinal disease trajectories.
  • Prediction of Protein-Protein Interactions (finished)
    Development of a novel deep learning architecture to predict virus-host protein-protein interactions.


Interview with Holger Fröhlich / 7.2.2024

»Kann KI Krankheiten anhand der Stimme erkennen?«

Interview with Holger Fröhlich

»Die Pharma-Branche befindet sich durch den Einsatz von KI in einer Umbruchphase«

Scientific publications

References to scientific publications prior to 2020


Examples of current research work in the group "AI & Data Science"



Talk by Holger Fröhlich on the AI World Congress 2021 in Seoul:

Keynote lecture at the 3rd Krems Dementia Congress (AIDEM 2021)