Prof. Kerstin Bunte, from the University of Groningen, will give an invited talk on “
Nowadays, most successful machine learning (ML) techniques for the analysis of complex interdisciplinary data use significant amounts of measurements as input to a statistical system. The domain expert knowledge is often only used in data preprocessing. The subsequently trained technique appears as a “black box”, which is difficult to interpret and rarely allows insight into the underlying natural process. Especially in critical domains such as medicine and engineering, the analysis of dynamic data in the form of sequences and me series is often difficult.
Due to natural or cost limitations and ethical considerations data is often irregularly and sparsely sampled and the underlying dynamic system is complex. Therefore, domain experts currently enter a me-consuming and laborious cycle of mechanistic model construction and simulation, often without direct use of the experimental data or the task at hand.
Recently, hybrid approaches were published combining the predictive power of ML and the explanatory power of pharmacokinetic models for model-based clustering (automatically determining groups of responses to medication in a clinical data set) and classification. In this talk I will give an overview of these new emerging strategies and the concept of “Learning in the model space” (LIMS).