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 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.

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.
    https://doi.org/10.1093/gigascience/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.
    https://www.nature.com/articles/s41598-018-29433-3
  • 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.
    https://www.nature.com/articles/s41746-022-00666-x
  • 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.
    https://academic.oup.com/brain/article/144/6/1738/6178276
  • 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. 
    https://doi.org/10.1002/alz.12387

Overview about our research projects

  • IDERHA
    AI based risk models using real-world data
  • Real4Reg
    AI models for real-world data
  • PsychSTRATA
    AI/ML models for precision psychiatry
  • CePPH
    AI/ML models for precision medicine
  • ParKInsonPredict
    AI for modeling of Parkinson's Disease progression
  • ADIS
    Coordinator, AI methods for biological system modeling and disease diagnosis
  • AIOLOS
    AI based models for early detection and monitoring of pandemic situations as well as decision support
  • DIGIPD
    Coordinator, AI methods for patient stratification
  • NFDI4Health
    AI methods for synthetic patient 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.

Collaboration

Interview mit 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

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

Team

Videos

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


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