Derzeitige Tätigkeit: Wissenschaftliche Mitarbeiterin bei Boehringer Ingelheim

Forschungsgebiet: I am interested in developing and applying machine learning algorithms to solve biomedical questions. In my previous work, I have successfully worked on enzyme function prediction based on cheminformatics descriptors and translated the methodology to predict the solvation free energy of crystalline drug-like molecules, an important question in virtual screening. Currently, I am interested to develop machine learning methods for medical applications, for instance, my work on using deep learning for peak annotation and ECG disease classification. Moreover, I have several years of experience processing and analyzing molecular OMICs data, e.g. in identifying radiation induced alteration in human gingiva fibroblasts from exome sequencing data, or in analyzing transcriptome data to characterize the impact of MALAT1 deficiency in atherosclerosis development. I have  worked with epidemiological and clinical data, and is currently involved in an ongoing project with the Study of Health in Pomerania (SHIP) consortium looking for novel biomarkers for thyroid dysfunction in the SHIP study, where I have employed different machine learning algorithms to mine genetic and clinical data and identified novel risk factors and their interrelation in disease progression. 

Programmteilnahme: Postdoktorandinnen 2019

Mentorin: Dr. Edda Schulz, Max-Planck-Institut für molekulare Genetik, Max Planck Institut Berlin

Interne Mentorin:Prof. Dr. Elke Krüger, Institut für Medizinische Biochemie und Molekularbiologie, Universitätsmedizin Greifswald