Deep learning for high-throuput data
||Deep learning for high-throuput data|
||Mika Gustafsson <firstname.lastname@example.org>|
||2023-10-20 – 2024-11-01|
A common problem with associating gene variants to complex disease associated traits is power and multiple testing. We have used gene networks to incorporate functional relevance among gene variants, but has not lead to decision support systems. In this project we aim to integrate network information with deep learning utilizing genomics, epigenomics and transcriptomics in millions of gene variants and 10000's of patients and integrating also protein binding prediction using alphaFOLD2. This project will store these data in an anonymous format when we compute as well as the resulting network models. The project is funded by grants from KAW and VR.