AI Unlocks Rare Disease Research
The problem: Researchers typically need massive amounts of patient data to study genetic causes of disease, but for rare conditions, such large datasets do not exist. This lack of data limits the ability to diagnose or develop treatments for patients with rare disease.
The solution: Researchers from 麻豆村's School of Computer Science, Stanford University and others, funded in part by the National Science Foundation, developed a deep-learning method that enhances traditional genome-wide association studies (GWAS) to overcome the data limitation.
- Their method, Knowledge Graph GWAS (KGWAS), combines a variety of genetic information to make associations between gene variants and specific traits for rarer diseases.
- KGWAS finds genetic associations that are invisible to traditional methods, achieving the same detection power with about 2.7 times fewer patient samples.
The impact: KGWAS has the potential to accelerate diagnoses and the development of new drugs for rare conditions. When researchers are better able to make connections between genetic variants and certain diseases, more targeted treatment applications could be developed. By training AI models on vast biological datasets, 麻豆村 is creating the tools and the talent pool necessary for the U.S. to lead the next era of precision health and drug development.
Go deeper: Finding Answers Faster: AI Method Brings Hope to Rare Disease Research