1. Machine Learning of System Biological Processes
Bachelor-/Master thesis Contact: Prof. Dr.-Ing. G. Dartmann
Prof. Dr.-Ing. A. Schmeink
Motivation
Use Case
Methodology
Sepsis is a life-threatening disease with high morbidity and mortality. Although novel therapeutics are urgently needed, the development of new drugs reveals a time and cost intensive worldwide challenge. The past few years have seen a profound development of machine learning techniques applied
in diverse fields. This knowledge offers a new component in the drug design process, which may revolutionize the way we develop drugs.
In addition to sepsis multiple medical applications are possible for this approach.
We investigate a novel holistic approach for a machine learning driven development of sepsis therapeutics using Hybrid models
Methods
Hypbrid Petri Nets
Machine Learning and Optimization
System and control technology
Machine Learning for model adaption
Discrete and continuous optimization
We use Hybrid Petri Nets to model the system biological process. In contrast to neural networks they offer the integration of expert knowledge.
P. Vieting, R. C. de Lamare, L. Martin, G. Dartmann and A. Schmeink, An Adaptive Learning Approach to Parameter Estimation for Hybrid Petri Nets in Systems Biology, IEEE Statistical Signal Processing Workshop (SSP), 2018
Tasks
Requirements
Research Questions
Students
Modelling of a simple example use case
Identification of training data for learning
Development of a methodology to adapt a Petri net based on training data
Information technology
Computer Science
Cooperation Partners
University Hospital Aachen, OIM
RWTH Aachen University, ICE & ISEK
Trier University of Applied Sciences