Approved research

Evaluation of deep learning in capturing epistatic effect

Lay summary

In this project we will study the power of deep learning approaches to infer information about the health status of an individual. For advanced risk predictions we will work with genetic as well as phenotypic data with as special focus on gene-gene interactions (epistasis). For this purpose we will use artificial neural networks, a technology that recently excelled in many challenging tasks of pattern recognition. Especially on medical image data such as funduscopies, these methods perform well and can be used to infer for instance cardiovascular risk factors, as shown by Poplin et al. (2018). At the beginning of our project we aim at validating recently published results, that were achieved with machine learning approaches on UK Biobank data sets. In a second phase of the project we will investigate whether the accuracy of a prediction can be improved if the phenotypic substructure of a cohort is taken into consideration. In the last phase, we will study whether models that go beyond additive effects of mutations, are able to explain more of the inherited risk to acquire a certain disease. The results of this project might not only be important for basic research -if it is possible to identify risk groups in the population more effectively, our methods will have a direct impact of public health, as they allow to provide precision care to those who will actually benefit most from it.