An effective similarity integration multi-modal graph neural network method to facilitate disease gene prioritization (A04)

• Subject Area: Bioinformatics and Theoretical Biology ; General Genetics and Functional Genome Biology
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 499552394

We will develop a machine learning approach that improves disease gene discovery by incorporating the similarity of genes and diseases respectively. In a proof-of-principle study, we will make use of a large neurodevelopmental disorder patient cohort, as well as other pediatric genetic disease cohorts. Specifically, we will develop an end-to-end multi-modal graph neural network for disease gene prioritization. This model will be evaluated in the disease cohorts and novel candidate genes will be experimentally confirmed by cell and animal models.

• DFG Programme: Collaborative Research Centres
• Subproject of: SFB 1597: Small Data
• Applicant Institution: Albert-Ludwigs-Universität Freiburg
• Project Heads: Professor Dr. Rolf Backofen; Dr. Miriam Schmidts