A research team at the Perelman School of Medicine at the University of Pennsylvania will use artificial intelligence to find patterns in genetic, imaging, and clinical data from over 60,000 Alzheimer’s patients. The goal of using AI is to identify new biomarkers of the disease.
The National Institute of Aging at NIH has provided a $17.8 million grant to the study, and Penn Medicine Investigators are teaming up with 11 research centres to determine more precise diagnostic biomarkers and drug targets for Alzheimer’s disease.
Li Shen, PhD, a professor of Informatics who will serve as two of five co-principal investigators on the five-year project, said, “We know that there are complex patterns in the brain that we may not be able to detect visually.
“Similarly, there may not be a single genetic marker that puts someone at high-risk for Alzheimer’s, but rather a combination of genes that may form a pattern and create a perfect storm.
“Machine learning can help to combine large datasets and tease out a complex pattern that couldn’t be seen before.”
Developing a relationship between the three modalities – genes, imaging, and clinical symptoms – in order to identify the patterns that predict Alzheimer’s diagnosis and progression is the initial goal of the project.
From that goal, researchers will then be able to distinguish between several different types of the disease.
Researchers will also use data from another NIH-funded effort the Alzheimer’s Disease Sequencing Project (ADSP). The aim of ADSP is to identify new genomic variants that contribute to, as well as ones that protect against, developing Alzheimer’s.
To find out more about this study, please visit the Penn Medicine News website.
