AI & Bioinformatics

To advance our understanding of diseases and their personalized treatment, the identification of genetic risk factors and their molecular signaling pathways, as well as the development of predictive models for disease progression and severity, are of the utmost importance. At MHH, existing and planned patient cohorts with state-of-the-art (single-cell) multi-omics data are available. Our junior research group focuses on the pre-processing (integration) of molecular data to generate standardized, high-quality datasets for the analyses conducted by other CAIMed junior research groups. The aim is to evaluate these data at scale using innovative AI-based methods. This includes identification of factors that correlate with disease severity and progression in order to predict individual responses to diseases and treatments, thereby establishing a molecular basis for the stratification of patient groups.

Currently, the group is engaged in the evaluation of datasets for the prediction of long-COVID, within the framework of the BMFTR-funded projects AID-PAIS and FEDCOV, which work on data from several German COVID-19 cohorts and the UK-Biobank. In addition, datasets from other chronic viral infections and associated fatigue syndromes (e.g., ME/CFS), such as those arising from chronic hepatitis C virus infections, are being integrated.

The overarching goal is to facilitate the translation of these mathematical models into clinical treatment, diagnostic, and predictive applications as a critical first step toward individualized prevention and therapy.