Prof. Dr. Maximilian Billmann

Our research aims to tackle three challenges in human genetics by developing computational methods. Those challenges include the limited statistical interpretability of sequencing studies, complex genetic dependencies as well as the limitation of translational biomedical approaches due to the inverse relation between genetic amenability and physiological relevance of genetic models. To aid statistical interpretability of, for instance, GWAS, we develop network-based computational methods that leverage information from the rapidly growing body of ‘Omics’ data including high-throughput CRISPR screens. Those latter data sets are particularly useful to also elucidate complex genetic dependencies. Here, we contribute to a large international collaborative effort to perform hundreds of genome-wide CRISPR-Cas9 screens to generate a reference network of complex genetic dependencies. Specifically, we develop methods to predict gene-gene and drug-gene interactions. Finally, we develop computational methods to tackle the paradoxical challenge in biomedical research: most ‘Omics’ data are recorded in non-human genetic models of simple human cell culture, which at least partially lacks a physiological disease-relevant context. We develop machine learning approaches to integrate orthogonal data, such as single cell gene expression data, to transfer conclusions form genetic studies to a more physiological context.