Deep learning for high-throuput data
Title: Deep learning for high-throuput data
DNr: NAISS 2023/6-201
Project Type: NAISS Medium Storage
Principal Investigator: Mika Gustafsson <>
Affiliation: Linköpings universitet
Duration: 2023-06-29 – 2024-07-01
Classification: 10203


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 images and gene binding networks. This project will store these data when we compute. The project is funded by grants from KAW, SSF and VR.