Clinical Bioinformatics

Our research aims to understand better cellular heterogeneity and the molecular mechanisms underlying childhood cancers. To achieve this, we leverage computational methods and comprehensive single-cell multiomics analyses, including spatial data, to uncover the molecular characteristics of individual cancer cells. We seek to uncover why some cancer cells develop resistance to treatment, leading to relapse, and to identify the most effective therapeutic strategies for pediatric cancer patients. You can also find us at the KiTZ.

Our team specializes in developing cutting-edge computational methodologies, leveraging machine learning and AI to enhance cancer diagnosis, classification, and drug response prediction. Through close collaborations with experimental lab partners, we apply single-cell multiomics approaches to tackle critical questions in cancer biology, such as therapy resistance. We are also a key contributor to the HEROES-AYA consortium (https://www.heroes-aya.de), where our emphasis lies in utilizing single-cell approaches to gain insights into tumor heterogeneity and the development of treatment resistance in sarcomas. Our long-term vision is to identify therapeutic targets through single-cell technologies and computational biology and advance drug discovery by applying AI-driven methodologies for pediatric cancers.