CAIMed Group 1b: AI and Bioinformatics

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 crucial for progress in the understanding and individualized treatment of diseases. At the MHH, existing and planned patient cohorts with state-of-the-art (single-cell) multi-omics data are available. The bioinformatics junior research group will focus on the pre-processing of molecular data in order to create standardized data sets for the analyses of the other CAIMed junior research groups. The aim is to integrate this data on an unprecedented scale using innovative AI methods. These include the identification of factors that correlate with disease severity and progression using causal inference methods. Furthermore, the investigation of cell type-specific genetic effects on molecular characteristics will be carried out using the "deconvolution" method. Finally, mathematical models such as support vector machines will be developed to predict individual reactions to diseases/treatments and thus create a molecular basis for the stratification of patient groups. The aim is to promote the implementation of these mathematical models in medical treatment or diagnostic procedures as a crucial first step towards individualized prevention. Close cooperation with the Integrative Multi-Omics Data group is planned.