Predicting Neurodegenerative Diseases With Deep Learning
posted October 30, 2024
A primary goal of genetic studies on complex diseases is to build predictive models to assess an individual's disease risk based on their genome. Current approaches primarily rely on collections of individual genetic variants identified through traditional genome-wide association study (GWAS) analysis and provide limited predictive accuracy. However, genes function in the context of a complex biological system, engaging in interactions with other genes to fulfill diverse functions. Thus, accurate disease risk prediction models will need to incorporate genetic interaction effects.
MSI PI Chad Myers (professor, Computer Science and Engineering) and his research group have developed a method, called BridGE, that provides a new, computational approach for discovering genetic interactions from GWAS data by incorporating knowledge of biological pathways. BridGE has been applied successfully to identify interactions for six diseases to date but has not yet been incorporated into individual disease risk prediction models. In a project called “Multi-task deep learning models for neurodegenerative disease risk prediction,” the group aims to build more accurate disease risk prediction models for neurodegenerative diseases (Parkinsons, Alzheimer’s disease, and ALS). They will leverage a state-of-the-art graph neural network deep learning approach that is specifically designed to capture interaction effects and enables joint learning of multiple human phenotypes simultaneously. Successful completion of the project will produce more accurate individual risk prediction models and new insights into the genetic basis of neurodegenerative disease.
This project received a Research Computing Seed Grant. RC Seed Grant funds promote, catalyze, accelerate and advance U of M-based informatics research.
As of Summer 2024, the RC Seed Grant programs have been revised into the DSI Seed Grant programs. These grants support innovative research in data science, fostering collaboration and advancing the field. The DSI focuses on key MnDrive areas: Foundational Data Sciences, Digital Health and Personalized Health Care Delivery, and Agriculture and the Environment. The types of awards are Rapid Response Grants, Awards for DSI Faculty Fellowship, and Data Sets (Data as an Asset).
Complete information about DSI Seed Grants, including application deadlines, can be found on the DSI website.
Image description: (A) Disease-associated pathways in a biological system (B) predict individual’s disease risk based their pathway-level genetic interaction profile.
