Eils Group, Hub for Innovations in Digital Health, Service center: Heidelberg Center for Human Bioinformatics – HD-HuB
Genetic contributions to many health-related phenotypes like blood pressure and body mass index often arise from complex interactions between multiple genes. Thus, an understanding of how variants of different genes interact, is of great interest. We plan to make use of recent advances in the field of explainable machine learning to analyze data from large cohort studies. Starting with variants of individual genes, we want to model how they affect biological systems, for example DNA repair. Based on the combination of smaller specific subsystems, we want to predict the activity of more complex systems and repeat this process until the information of all genes is combined in one system describing the effect of all variants. At that point, the information on thousands of genes will be available in a very compressed representation, which will be the basis to predict phenotypes like blood pressure or other medically relevant phenotypes.
Additionally, this approach can be used to investigate which cellular systems in the model are affected by a given set of variants resulting in a specific phenotype. For example, for an individual with increased blood pressure and variants in multiple genes, this could be used to predict, whether the DNA repair system is involved in the increased blood pressure. Information like this can be used to check if the model makes its predictions in an intuitive way and can also be the starting point of new research, if strong connections are found.
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