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Stef Zeemering

Stef Zeemering, Postdoctoral fellow at the Department of Physiology at Maastricht University / Maastricht University Medical Centre (MUMC+)

My RACE V YTP-funded project is aimed at developing a systems-genomics approach to the analysis of atrial fibrillation. 

Originally trained in mathematics and computer science, I have a background in system identification, parameter estimation and data mining in general. After obtaining a PhD in the field of systems identification applied to atrial fibrillation, my research has been focussed on the quantification of atrial fibrillation complexity, based either on analysis of invasive measurements of AF (high-density mapping), or non-invasive measurements, most notably the atrial activity that can be extracted from a 12-lead ECG or a body surface potential map. Proper non-invasive quantification of AF complexity has a promising application in the prediction of AF treatment outcome in different stages of AF, for instance prediction of successful cardioversion of recent onset (self-terminating) AF and prediction of recurrence after electrical cardioversion or ablation of non-self-terminating AF.

The next step in my career is to develop myself further in the field of (cardiovascular) systems biology, to bridge the gap between the individual clinical characteristics of a patient and the molecular mechanisms that they reflect. To this extent, the data that will be collected in RACE V provides an excellent opportunity to investigate the link between atrial gene expression and atrial remodelling and the relationship between clinical characteristics and atrial tissue characteristics. Here we will be able to integrate many different types of measurements into a multimodal model for classification and prediction of AF progression, applying state-of-the-art bio-statistical analysis and feature selection techniques. The YTP program has enabled me to organize a 6-month stay at the Westfählische Wilhems-Universität in Münster, where I followed extensive training in the acquisition and analysis of complex, multifactorial genetic data, in the research group of prof. Stoll. In this way I could expand my current expertise in the field of data mining and machine learning to include the novel field of systems-genomics. The experience and results that I gathered from the analysis of a pilot dataset similar to the data collected in RACE V, will serve as an excellent starting point for the processing and analysis of the RACE V data.