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Knowledge Discovery and Synthesis

The "Knowledge Discovery and Synthesis" group is investigating methods for identifying potentially complex patterns in data, and methods for synthesizing information from several sources. The spectrum of our work ranges from meta-analysis techniques for clinical trials to machine learning techniques, in particular artificial intelligence/deep learning, for integrating molecular and clinical data.

Projects

Publications

 

Head

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Prof. Dr. Harald Binder

Machine Learning (esp. Deep Learning)
Integration of Molecular and Clinical Data

 

Members

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Dr. Federico Bonofiglio


Maximum Entropy Methods
Event Histroy Analysis
Research Synthesis
Machine Learning (esp. Deep Learning)

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Dr. Moritz Hess

Machine Learning (esp. Deep Learning)
Feature Learning in High-Dimensional Molecular-Diagnostic Data

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Yessica Fermin

STRATOS
Multivariable Modeling
Fractional Polynomials
Detection of Influential Observations

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Ewelina Kotwa

STRATOS

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Göran Köber

Machine Learning (esp. Deep Learning)
Resilience and Vulnerabilty
Age-Period-Cohort Analysis

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Stefan Lenz

Algorithms, (esp. in the Field of Deep Learning)
Software Development and API Design

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Dr. Gerta Rücker

Meta-Analysis
Network Meta-Analysis
Meta-Analysis of Diagnostic Accuracy Studies

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Martin Treppner

Deep Generative Models
Design of Single Cell RNA-Seq Experiments

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Prof. Dr. Willi Sauerbrei

STRATOS

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Dr. Guido Schwarzer

Meta-Analysis
Software Development

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Dr. Daniela Zöller

Longitudinal Data Modeling
Modeling of Clinical Registry Data
Modeling of Distributed Data