Consolidated Researcher Details:
Biology and mathematics were my favorite subjects in school and therefore, I studied bioinformatics at the Friedrich-Wilhelms-University in Bonn and at Bielefeld University. In this programme, I also had the opportunity to take lab courses in genetics and genomics so that I was able to perform some of the microarray experiments that I analyzed in my diploma thesis.
After graduating in 2005, I wanted to learn more about statistics and contribute towards answering medical research questions. I joined the Institute of Medical Biometry and Statistics at the University of Lübeck (supervisor: Prof. Dr. rer. nat. Andreas Ziegler) and worked on a wide range of projects analyzing genome-wide association studies, gene expression and proteomics experiments. In my methods-orientated PhD thesis, I evaluated different statistical tests for eQTL studies based on simulated data.
From 2011 to 2013, I was a postdoctoral fellow at the Inherited Disease Research Branch of the National Human Genome Research Institute, NIH in Baltimore, USA (supervisor: Dr. Joan Bailey-Wilson). I analyzed data from linkage scans and whole exome sequencing in family studies. My methodological work focused on machine learning methods, especially random forests, for identifying genetic variants contributing to complex diseases.
From March 2013 until August 2014 I was member of Prof. Andre Franke’s group at the Institute of Clinical Molecular Biology, Kiel. Since September 2014, I am research scientist at the Institute of Medical Informatics and Statistics, Kiel.
Starting in September 2016, I will be leader of the BMBF funded junior research group ComorbSysMed - Comorbidity patterns in inflammatory skin diseases: A systems medicine approach using machine learning and omics technologies.
Complex diseases such as inflammation are caused by many interacting genetic and environmental factors. High-throughput technologies that generate data from different molecular levels (e.g. genetics, transcriptomics, epigenetics) in combination with sophisticated statistical and computational methods are a promising approach to elucidate the molecular basis of these complex diseases. The interdisciplinary research environment within the Research Training Group provides an excellent opportunity to work at the interface of biology/genetics/medicine and statistics/bioinformatics.