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 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.

For more information, including publications see http://abi.bit.uni-bonn.de.

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.
  • Benjamin Engelhardt, Maik Kschischo, Holger Fröhlich (2017), A Bayesian Approach to Estimating Hidden Variables as well as Missing and Wrong Molecular Interactions in ODE Based Mathematical Models, Journal of the Royal Society Interface, Jun;14(131). 14:20170332
  • Ashar Ahmad, Holger Fröhlich (2017), Towards Clinically More Relevant Dissection of Patient Heterogeneity via Survival based Bayesian Clustering, Bioinformatics, 33(22), 3558 - 3566
  • Y. Cun, H. Fröhlich (2013), Network and Data Integration for Biomarker Signature Discovery via Network Smoothed T-Statistics, PLoS ONE, 8(9):e73074

Projects and our role

  • DIGIPD
    Evaluation of the utility of digital technology (gait sensors, voice and face movement analysis) for better diagnosis and individualized prognosis of Parkinson’s Disease. SCAI will contribute AI methods for clustering of multivariate clinical patient trajectories and for disentangling relationships between digital biomarkers, clinical measures and molecular mechanisms.
  • The Virtual Brain Cloud
    AI-based progression models
  • RADAR-AD
    AI-based progression models
  • IDSN
    Text Mining
  • NFDI4Health (starts in November)
    AI methods for synthetic patient data
  • COPERIMOplus
    AI based risk models for Covid-19
  • AETIONOMY (finished)
 

Collaboration

Interview mit Holger Fröhlich

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

Recent academic work results

Examples

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